Dissertations / Theses: 'Simultaneous localisation and mapping'

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Eade, Ethan. "Monocular simultaneous localisation and mapping." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611415.

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Parsley, M. P. "Simultaneous localisation and mapping with prior information." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1318103/.

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This thesis is concerned with Simultaneous Localisation and Mapping (SLAM), a technique by which a platform can estimate its trajectory with greater accuracy than odometry alone, especially when the trajectory incorporates loops. We discuss some of the shortcomings of the "classical" SLAM approach (in particular EKF-SLAM), which assumes that no information is known about the environment a priori. We argue that in general this assumption is needlessly stringent; for most environments, such as cities some prior information is known. We introduce an initial Bayesian probabilistic framework which considers the world as a hierarchy of structures, and maps (such as those produced by SLAM systems) as consisting of features derived from them. Common underlying structure between features in maps allows one to express and thus exploit geometric relations between them to improve their estimates. We apply the framework to EKF-SLAM for the case of a vehicle equipped with a range-bearing sensor operating in an urban environment, building up a metric map of point features, and using a prior map consisting of line segments representing building footprints. We develop a novel method called the Dual Representation, which allows us to use information from the prior map to not only improve the SLAM estimate, but also reduce the severity of errors associated with the EKF. Using the Dual Representation, we investigate the effect of varying the accuracy of the prior map for the case where the underlying structures and thus relations between the SLAM map and prior map are known. We then generalise to the more realistic case, where there is "clutter" - features in the environment that do not relate with the prior map. This involves forming a hypothesis for whether a pair of features in the SLAMstate and prior map were derived from the same structure, and evaluating this based on a geometric likelihood model. Initially we try an incrementalMultiple Hypothesis SLAM(MHSLAM) approach to resolve hypotheses, developing a novel method called the Common State Filter (CSF) to reduce the exponential growth in computational complexity inherent in this approach. This allows us to use information from the prior map immediately, thus reducing linearisation and EKF errors. However we find that MHSLAM is still too inefficient, even with the CSF, so we use a strategy that delays applying relations until we can infer whether they apply; we defer applying information from structure hypotheses until their probability of holding exceeds a threshold. Using this method we investigate the effect of varying degrees of "clutter" on the performance of SLAM.

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Williams, Emily. "Simultaneous localisation and mapping for surveying applications." Thesis, University of Nottingham, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.755820.

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Mobile Laser Scanning (MLS) is a data capture technique that has transformed the way in which survey data is collected especially for infrastructure corridors such as highways and railways. Using an integrated GPS and Inertial Measuring Unit (IMU), the mobile vehicle and therefore the collected data has a proven accuracy of +/- 10mm, when using ground control points. This allows high accuracy data collection at normal highway speeds. The main limitation of this type of data capture is the reliance on GPS for positioning. Although blackouts in GPS signal can be bridged using the IMU, the resulting error follows a time dependent model resulting in large errors after short periods of time. This prohibits the use of MLS for high accuracy applications in areas such as indoors and underground. Simultaneous Localisation and Mapping (SLAM) is a robotics technique that collects data about the environment as it navigates in order to make deeisionson its movement creating a completely autonomous robot. The main advantage of SLAM for mapping is that the robot does not rely on GPS to navigate its surroundings. Instead it relies wholly on relative measurements to the environment in which it travels. The maps created are a biproduct of the navigation solution and the accuracy of them is often overlooked. In order to use the maps created for purposes other than autonomy it is important that their creation is investigated and the final maps are critically assessed. This thesis is an investigation into using SLAM for the creation of high accuracy maps. Analysis is undertaken to assess the ability of these maps to be used for surveying applications. Initially an investigation is undertaken to understand the processes involved for the production of SLAM maps. With the primary outcome being to determine the perceived benefits of using the maps created using SLAM techniques for mobile mapping applications. Trials are undertaken to critically evaluate the capability of typical sensors used on board SLAM systems for the production of reliable measurements. The Zebedee system, developed by CSIRO is identified as a mapping tool which uses SLAM techniques. An error model is developed for this system using a traditional survey approach, the results of which are compared against data collected in an indoor and an underground environment. The resulting point clouds from the test sites are assessed to determine the surveying applications which this type of system can provide data for.

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Nemra, A. "Robust airborne 3D visual simultaneous localisation and mapping." Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/6157.

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The aim of this thesis is to present robust solutions to technical problems of airborne three-dimensional (3D) Visual Simultaneous Localisation And Mapping (VSLAM). These solutions are developed based on a stereovision system available onboard Unmanned Aerial Vehicles (UAVs). The proposed airborne VSLAM enables unmanned aerial vehicles to construct a reliable map of an unknown environment and localise themselves within this map without any user intervention. Current research challenges related to Airborne VSLAM include the visual processing through invariant feature detectors/descriptors, efficient mapping of large environments and cooperative navigation and mapping of complex environments. Most of these challenges require scalable representations, robust data association algorithms, consistent estimation techniques, and fusion of different sensor modalities. To deal with these challenges, seven Chapters are presented in this thesis as follows: Chapter 1 introduces UAVs, definitions, current challenges and different applications. Next, in Chapter 2 we present the main sensors used by UAVs during navigation. Chapter 3 presents an important task for autonomous navigation which is UAV localisation. In this chapter, some robust and optimal approaches for data fusion are proposed with performance analysis. After that, UAV map building is presented in Chapter 4. This latter is divided into three parts. In the first part, a new imaging alternative technique is proposed to extract and match a suitable number of invariant features. The second part presents an image mosaicing algorithm followed by a super-resolution approach. In the third part, we propose a new feature detector and descriptor that is fast, robust and detect suitable number of features to solve the VSLAM problem. A complete Airborne Visual Simultaneous Localisation and Mapping (VSLAM) solution based on a stereovision system is presented in Chapter (5). Robust data association filters with consistency and observability analysis are presented in this chapter as well. The proposed algorithm is validated with loop closing detection and map management using experimental data. The airborne VSLAM is extended then to the multiple UAVs case in Chapter (6). This chapter presents two architectures of cooperation: a Centralised and a Decentralised. The former provides optimal precision in terms of UAV positions and constructed map while the latter is more suitable for real time and embedded system applications. Finally, conclusions and future works are presented in Chapter (7).

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Mountney, Peter Edward. "simultaneous localisation and mapping for minimally invasive surgery." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.530462.

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Williams, Brian P. "Simultaneous localisation and mapping using a single camera." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:2fccd8f6-170d-4c5b-b77b-7e371cad4df6.

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This thesis describes a system which is able to track the pose of a hand-held camera as it moves around a scene. The system builds a 3D map of point landmarks in the world while tracking the pose of the camera relative to this map using a process called simultaneous localisation and mapping (SLAM). To achieve real-time performance, the map must be kept sparse, but rather than observing only the mapped landmarks like previous systems, observations are made of features across the entire image. Their deviation from the predicted epipolar geometry is used to further constrain the estimated inter-frame motion and so improves the overall accuracy. The consistency of the estimation is also improved by performing the estimation in a camera-centred coordinate frame. As with any such system, tracking failure is inevitable due to occlusion or sudden motion of the camera. A relocalisation module is presented which monitors the SLAM system, detects tracking failure, and then resumes tracking as soon as the conditions have improved. This relocalisation process is achieved using a new landmark recognition algorithm which is trained on-line and provides high recall and a fast recognition time. The relocalisation module can also be used to achieve place recognition for a loop closure detection system. By taking into account both the geometry and appearance information when determining a loop closure this module is able to outperform previous loop closure detection techniques used in monocular SLAM. After recognising an overlap, the map is then corrected using a novel trajectory alignment technique that is able to cope with the inherent scale ambiguity in monocular SLAM. By incorporating all of these new techniques, the system presented can perform as a robust augmented reality system, or act as a navigation tool which could be used on a mobile robot in indoor and outdoor environments.

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Sinivaara, Kristian. "Simultaneous Localisation and Mapping using Autonomous Target Detection and Recognition." Thesis, Linköpings universitet, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110410.

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Simultaneous localisation and mapping (SLAM) is an often used positioning approach in GPS denied indoor environments. This thesis presents a novel method of combining SLAM with autonomous/aided target detection and recognition (ATD/R), which is beneficial for both methods. The method uses physical objects that are recognisable by ATR as unambiguous features in SLAM, while SLAM provides the ATR with better position estimates. The intended application is to improve the positioning of a first responder moving through an indoor environment, where the map offers localisation and simultaneously helps locate people, furniture and potentially dangerous objects like gas cannisters. The developed algorithm, dubbed ATR-SLAM, uses existing methods from different fields such as EKF-SLAM and ATR based on rectangle estimation. Landmarks in the form of 3D point features based on NARF are used in conjunction with identified objects and 3D object models are used to replace landmarks when the same information is used. This leads to a more compact map representation with fewer landmarks, which partly compensates for the introduced cost of the ATR. Experiments performed using point clouds generated from a time-of-flight laser scanner show that ATR-SLAM produces more consistent maps and more robust loop closures than EKF-SLAM using only NARF landmarks.

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Barkby, Stephen. "Efficient and Featureless Approaches to Bathymetric Simultaneous Localisation and Mapping." Thesis, The University of Sydney, 2011. http://hdl.handle.net/2123/7919.

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This thesis investigates efficient forms of Simultaneous Localization and Mapping (SLAM) that do not require explicit identification, tracking or association of map features. The specific application considered here is subsea robotic bathymetric mapping. In this context, SLAM allows a GPS-denied robot operating near the sea floor to create a self-consistent bathymetric map. This is accomplished using a Rao-Blackwellized Particle Filter (RBPF) whereby each particle maintains a hypothesis of the current vehicle state and map that is efficiently maintained using Distributed Particle Mapping. Through particle weighting and resampling, successive observations of the seafloor structure are used to improve the estimated trajectory and resulting map by enforcing map self consistency. The main contributions of this thesis are two novel map representations, either of which can be paired with the RBPF to perform SLAM. The first is a grid-based 2D depth map that is efficiently stored by exploiting redundancies between different maps. The second is a trajectory map representation that, instead of directly storing estimates of seabed depth, records the trajectory of each particle and synchronises it to a common log of bathymetric observations. Upon detecting a loop closure each particle is weighted by matching new observations to the current predictions. For the grid map approach this is done by extracting the predictions stored in the observed cells. For the trajectory map approach predictions are instead generated from a local reconstruction of their map using Gaussian Process Regression. While the former allows for faster map access the latter requires less memory and fully exploits the spatial correlation in the environment, allowing predictions of seabed depth to be generated in areas that were not directly observed previously. In this case particle resampling therefore not only enforces self-consistency in overlapping sections of the map but additionally enforces self-consistency between neighboring map borders. Both approaches are validated using multibeam sonar data collected from several missions of varying scale by a variety of different Unmanned Underwater Vehicles. These trials demonstrate how the corrections provided by both approaches improve the trajectory and map when compared to dead reckoning fused with Ultra Short Baseline or Long Baseline observations. Furthermore, results are compared with a pre-existing state of the art bathymetric SLAM technique, confirming that similar results can be achieved at a fraction of the computation cost. Lastly the added capabilities of the trajectory map are validated using two different bathymetric datasets. These demonstrate how navigation and mapping corrections can still be achieved when only sparse bathymetry is available (e.g. from a four beam Doppler Velocity Log sensor) or in missions where map overlap is minimal or even non-existent.

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Agarwal, Saurav. "Monocular vision based indoor simultaneous localisation and mapping for quadrotor platform." Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7210.

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An autonomous robot acting in an unknown dynamic environment requires a detailed understanding of its surroundings. This information is provided by mapping algorithms which are necessary to build a sensory representation of the environment and the vehicle states. This aids the robot to avoid collisions with complex obstacles and to localize in six degrees of freedom i.e. x, y, z, roll, pitch and yaw angle. This process, wherein, a robot builds a sensory representation of the environment while estimating its own position and orientation in relation to those sensory landmarks, is known as Simultaneous Localisation and Mapping (SLAM). A common method for gauging environments are laser scanners, which enable mobile robots to scan objects in a non-contact way. The use of laser scanners for SLAM has been studied and successfully implemented. In this project, sensor fusion combining laser scanning and real time image processing is investigated. Hence, this project deals with the implementation of a Visual SLAM algorithm followed by design and development of a quadrotor platform which is equipped with a camera, low range laser scanner and an on-board PC for autonomous navigation and mapping of unstructured indoor environments. This report presents a thorough account of the work done within the scope of this project. It presents a brief summary of related work done in the domain of vision based navigation and mapping before presenting a real time monocular vision based SLAM algorithm. A C++ implementation of the visual slam algorithm based on the Extended Kalman Filter is described. This is followed by the design and development of the quadrotor platform. First, the baseline speci cations are described followed by component selection, dynamics modelling, simulation and control. The autonomous navigation algorithm is presented along with the simulation results which show its suitability to real time application in dynamic environments. Finally, the complete system architecture along with ight test results are described.

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Ihemadu, Okechukwu Clifford. "Robotic navigation in large environments using simultaneous localisation and mapping (SLAM)." Thesis, Queen's University Belfast, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.602546.

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This thesis provides techniques to address some outstanding problems in robotic navigation in relatively large environments using simultaneous localisation and mapping (SLAM), resulting in improved competence and reliability of autonomous agents. Autonomous mobile agents are helpful in a diverse range of applications deemed as dull, dirty or dangerous for human operators including mining, defence and underwater explorations. In order to be fully autonomous the robot is required to incrementally construct a map of its vicinity and simultaneously localise itself within the environment based only on its on-board sensory data. However, the robot is confronted with a number of serious challenges that impair large-scale navigation and could even render SLAM results intractable in real time. This work provides efficient strategies for addressing such problems. The techniques presented include an effective method of reducing computational burden of updating the covariance matrix at every step, and cutting down the storage requirements. In addition, the usually complex and tedious task of transforming and fusing sub-maps into a single global map is simplified. Furthermore, an intelligent SLAM (I -SLAM) is introduced that enables accurate place recognition and minimise pose estimation errors. It also provides the means of adaptively adjusting to the nature of natural surface terrains for both indoor and outdoor environments. These techniques promise an enormous potential for autonomous agents operating in unknown environments in terms of consistency, accuracy and efficiency. Simulation results demonstrate the reliability and effectiveness of the system as a means of addressing some of the outstanding challenges with regards to large-scale SLAM performance

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Williams, Stefan Bernard. "Efficient Solutions to Autonomous Mapping and Navigation Problems." University of Sydney. Aerospace, Mechanical and Mechatronic Engineering, 2002. http://hdl.handle.net/2123/809.

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This thesis deals with the Simultaneous Localisation and Mapping algorithm as it pertains to the deployment of mobile systems in unknown environments. Simultaneous Localisation and Mapping (SLAM) as defined in this thesis is the process of concurrently building up a map of the environment and using this map to obtain improved estimates of the location of the vehicle. In essence, the vehicle relies on its ability to extract useful navigation information from the data returned by its sensors. The vehicle typically starts at an unknown location with no a priori knowledge of landmark locations. From relative observations of landmarks, it simultaneously computes an estimate of vehicle location and an estimate of landmark locations. While continuing in motion, the vehicle builds a complete map of landmarks and uses these to provide continuous estimates of the vehicle location. The potential for this type of navigation system for autonomous systems operating in unknown environments is enormous. One significant obstacle on the road to the implementation and deployment of large scale SLAM algorithms is the computational effort required to maintain the correlation information between features in the map and between the features and the vehicle. Performing the update of the covariance matrix is of O(n�) for a straightforward implementation of the Kalman Filter. In the case of the SLAM algorithm, this complexity can be reduced to O(n�) given the sparse nature of typical observations. Even so, this implies that the computational effort will grow with the square of the number of features maintained in the map. For maps containing more than a few tens of features, this computational burden will quickly make the update intractable - especially if the observation rates are high. An effective map-management technique is therefore required in order to help manage this complexity. The major contributions of this thesis arise from the formulation of a new approach to the mapping of terrain features that provides improved computational efficiency in the SLAM algorithm. Rather than incorporating every observation directly into the global map of the environment, the Constrained Local Submap Filter (CLSF) relies on creating an independent, local submap of the features in the immediate vicinity of the vehicle. This local submap is then periodically fused into the global map of the environment. This representation is shown to reduce the computational complexity of maintaining the global map estimates as well as improving the data association process by allowing the association decisions to be deferred until an improved local picture of the environment is available. This approach also lends itself well to three natural extensions to the representation that are also outlined in the thesis. These include the prospect of deploying multi-vehicle SLAM, the Constrained Relative Submap Filter and a novel feature initialisation technique. Results of this work are presented both in simulation and using real data collected during deployment of a submersible vehicle equipped with scanning sonar.

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Joubert, Deon. "Saliency grouped landmarks for use in vision-based simultaneous localisation and mapping." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/40834.

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The effective application of mobile robotics requires that robots be able to perform tasks with an extended degree of autonomy. Simultaneous localisation and mapping (SLAM) aids automation by providing a robot with the means of exploring an unknown environment while being able to position itself within this environment. Vision-based SLAM benefits from the large amounts of data produced by cameras but requires intensive processing of these data to obtain useful information. In this dissertation it is proposed that, as the saliency content of an image distils a large amount of the information present, it can be used to benefit vision-based SLAM implementations. The proposal is investigated by developing a new landmark for use in SLAM. Image keypoints are grouped together according to the saliency content of an image to form the new landmark. A SLAM system utilising this new landmark is implemented in order to demonstrate the viability of using the landmark. The landmark extraction, data filtering and data association routines necessary to make use of the landmark are discussed in detail. A Microsoft Kinect is used to obtain video images as well as 3D information of a viewed scene. The system is evaluated using computer simulations and real-world datasets from indoor structured environments. The datasets used are both newly generated and freely available benchmarking ones.
Dissertation (MEng)--University of Pretoria, 2013.
gm2014
Electrical, Electronic and Computer Engineering
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Jung, Il-Kyun. "Simultaneous localization and mapping in 3D environments with stereovision." Toulouse, INPT, 2004. http://www.theses.fr/2004INPT004H.

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Dans cette thèse, nous abordons le problème SLAM pour des robots évoluant en 3D dans de grands environnements, en utilisant la stéréovision. Une implémentation complète des différentes fonctionnalités nécessaires a été conçue, développée et expérimentée dans différents contextes. La première partie de la thèse traite du problème d'association des données : elle présente un algorithme de mise en correspondance de points d'intérêt détectés dans les images, qui est robuste par rapport au bruit et aux changements de point de vue. La deuxième partie de la thèse est dédiée au développement d'une approche du problème SLAM basée sur le filtrage de Kalman. Les amers sont les points d'intérêt détectés dans les images, dont les coordonnées 3D sont fournies par la stéréovision. La dernière partie de la thèse présente et analyse des résultats obtenus dans différents contextes : sur des trajectoires de plusieurs centaines de mètres effectuées par un ballon dirigeable évoluant à faible altitude, avec un robot évoluant en environnement naturel non structuré, et avec un robot évoluant en environnements intérieurs. Lorsque la trajectoire ``ferme une boucle'', une méthode rapide de correction de l'estimée des différentes positions par lesquelles le robot est passé permet de reconstruire un modèle numérique du terrain.

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Harribhai, Jatin I. "Using regions of interest to track landmarks for RGBD simultaneous localisation and mapping." Master's thesis, Faculty of Engineering and the Built Environment, 2019. http://hdl.handle.net/11427/31057.

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The simultaneous localisation and mapping (SLAM) algorithm have been widely used for autonomous navigation of robots. A type of visual SLAM that is popular among the researchers is RGBD SLAM. However processing immense image data to identify and track landmarks in RGBD SLAM can be computationally expensive for smaller robots. This dissertation presents an alternate method to reduce the computational time. The proposed algorithm extracts features from a region of interest (ROI) to track landmarks for RGBD SLAM. This strategy is compared to the traditional method of extracting features from an entire image. The ROI algorithm is implemented via a pre-processing algorithm, which is then integrated into the RGBD SLAM framework. The pre-processing pipeline implements image processing algorithms in three stages to process the data. Stage one uses a ROI algorithm to detect ROIs in an image. For visual SLAM such as RGBD SLAM, objects that are highly detailed are used as landmarks. Hence the ROI algorithm is designed to detect ROIs containing highly detailed objects. Stage two extracts features from the image and stage three uses feature matching algorithms to re-identify a ROI. Once a ROI has been successfully re-identified, it is stored and categorised as a landmark for RGBD SLAM. Scale invariant feature transform (SIFT), speeded up robust features (SURF) and orientated FAST and rotated BRIEF (ORB) are three feature extraction algorithms that are used in stage two. The outcomes from this study revealed that the pipeline was able to successfully create a database of landmarks which can be re-identified in subsequent frames. In addition, the results showed that when the pipeline is configured such that SURF features are used with a bigger ROI, RGBD SLAM produced more accurate results in determining the position of the robot compared to the traditional method of extracting features from an entire image. However, this strategy requires more computational time. The findings further revealed that this strategy still out performs the traditional method when the number of features extracted is reduced. This indicated that this strategy performs more robustly compared to the traditional method in environments that can contain few features. The method presented in this study did not improve the computational time of RGBD SLAM but did improve the accuracy in localizing the robot.

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Werner, Felix. "Vision-based topological mapping and localisation." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/31815/1/Felix_Werner_Thesis.pdf.

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Competent navigation in an environment is a major requirement for an autonomous mobile robot to accomplish its mission. Nowadays, many successful systems for navigating a mobile robot use an internal map which represents the environment in a detailed geometric manner. However, building, maintaining and using such environment maps for navigation is difficult because of perceptual aliasing and measurement noise. Moreover, geometric maps require the processing of huge amounts of data which is computationally expensive. This thesis addresses the problem of vision-based topological mapping and localisation for mobile robot navigation. Topological maps are concise and graphical representations of environments that are scalable and amenable to symbolic manipulation. Thus, they are well-suited for basic robot navigation applications, and also provide a representational basis for the procedural and semantic information needed for higher-level robotic tasks. In order to make vision-based topological navigation suitable for inexpensive mobile robots for the mass market we propose to characterise key places of the environment based on their visual appearance through colour histograms. The approach for representing places using visual appearance is based on the fact that colour histograms change slowly as the field of vision sweeps the scene when a robot moves through an environment. Hence, a place represents a region of the environment rather than a single position. We demonstrate in experiments using an indoor data set, that a topological map in which places are characterised using visual appearance augmented with metric clues provides sufficient information to perform continuous metric localisation which is robust to the kidnapped robot problem. Many topological mapping methods build a topological map by clustering visual observations to places. However, due to perceptual aliasing observations from different places may be mapped to the same place representative in the topological map. A main contribution of this thesis is a novel approach for dealing with the perceptual aliasing problem in topological mapping. We propose to incorporate neighbourhood relations for disambiguating places which otherwise are indistinguishable. We present a constraint based stochastic local search method which integrates the approach for place disambiguation in order to induce a topological map. Experiments show that the proposed method is capable of mapping environments with a high degree of perceptual aliasing, and that a small map is found quickly. Moreover, the method of using neighbourhood information for place disambiguation is integrated into a framework for topological off-line simultaneous localisation and mapping which does not require an initial categorisation of visual observations. Experiments on an indoor data set demonstrate the suitability of our method to reliably localise the robot while building a topological map.

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JUNG, Il Kyun. "Simultaneous localization and mapping in 3D environments with stereovision." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2004. http://tel.archives-ouvertes.fr/tel-00010250.

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Dans cette thèse, nous abordons le problème SLAM pour des robots évoluant en 3D dans de grands environnements, en utilisant la stéréovision. Une implémentation complète des différentes fonctionnalités nécessaires a été conçue, développée et expérimentée dans différents contextes. La première partie de la thèse traite du problème d'association des données :elle présente un algorithme de mise en correspondance de points d'intérêt détectés dans les images, qui est robuste par rapport au bruit et aux changements de point de vue. La deuxième partie de la thèse est dédiée au développement d'une approche du problème SLAM basée sur le filtrage de Kalman. Les amers sont les points d'intérêt détectés dans les images, dont les coordonnées 3D sont fournies par la stéréovision. La dernière partie de la thèse présente et analyse des résultats obtenus dans différents contextes~: sur des trajectoires de plusieurs centaines de mètres effectuées par un ballon dirigeable évoluant à faible altitude, avec un robot évoluant en environnement naturel non structuré, et avec un robot évoluant en environnements intérieurs. Lorsque la trajectoire ``ferme une boucle'', une méthode rapide de correction de l'estimée des différentes positions par lesquelles le robot est passé permet de reconstruire un modèle numérique du terrain.

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Ramos, Fabio Tozeto. "Recognising, Representing and Mapping Natural Features in Unstructured Environments." Australian Centre for Field Robotics, Department of Aerospace, Mechanical and Mechatronic Engineering, 2008. http://hdl.handle.net/2123/2322.

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Doctor of Philosophy (PhD)
This thesis addresses the problem of building statistical models for multi-sensor perception in unstructured outdoor environments. The perception problem is divided into three distinct tasks: recognition, representation and association. Recognition is cast as a statistical classification problem where inputs are images or a combination of images and ranging information. Given the complexity and variability of natural environments, this thesis investigates the use of Bayesian statistics and supervised dimensionality reduction to incorporate prior information and fuse sensory data. A compact probabilistic representation of natural objects is essential for many problems in field robotics. This thesis presents techniques for combining non-linear dimensionality reduction with parametric learning through Expectation Maximisation to build general representations of natural features. Once created these models need to be rapidly processed to account for incoming information. To this end, techniques for efficient probabilistic inference are proposed. The robustness of localisation and mapping algorithms is directly related to reliable data association. Conventional algorithms employ only geometric information which can become inconsistent for large trajectories. A new data association algorithm incorporating visual and geometric information is proposed to improve the reliability of this task. The method uses a compact probabilistic representation of objects to fuse visual and geometric information for the association decision. The main contributions of this thesis are: 1) a stochastic representation of objects through non-linear dimensionality reduction; 2) a landmark recognition system using a visual and ranging sensors; 3) a data association algorithm combining appearance and position properties; 4) a real-time algorithm for detection and segmentation of natural objects from few training images and 5) a real-time place recognition system combining dimensionality reduction and Bayesian learning. The theoretical contributions of this thesis are demonstrated with a series of experiments in unstructured environments. In particular, the combination of recognition, representation and association algorithms is applied to the Simultaneous Localisation and Mapping problem (SLAM) to close large loops in outdoor trajectories, proving the benefits of the proposed methodology.

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Ramos, Fabio Tozeto. "Recognising, Representing and Mapping Natural Features in Unstructured Environments." Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/2322.

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This thesis addresses the problem of building statistical models for multi-sensor perception in unstructured outdoor environments. The perception problem is divided into three distinct tasks: recognition, representation and association. Recognition is cast as a statistical classification problem where inputs are images or a combination of images and ranging information. Given the complexity and variability of natural environments, this thesis investigates the use of Bayesian statistics and supervised dimensionality reduction to incorporate prior information and fuse sensory data. A compact probabilistic representation of natural objects is essential for many problems in field robotics. This thesis presents techniques for combining non-linear dimensionality reduction with parametric learning through Expectation Maximisation to build general representations of natural features. Once created these models need to be rapidly processed to account for incoming information. To this end, techniques for efficient probabilistic inference are proposed. The robustness of localisation and mapping algorithms is directly related to reliable data association. Conventional algorithms employ only geometric information which can become inconsistent for large trajectories. A new data association algorithm incorporating visual and geometric information is proposed to improve the reliability of this task. The method uses a compact probabilistic representation of objects to fuse visual and geometric information for the association decision. The main contributions of this thesis are: 1) a stochastic representation of objects through non-linear dimensionality reduction; 2) a landmark recognition system using a visual and ranging sensors; 3) a data association algorithm combining appearance and position properties; 4) a real-time algorithm for detection and segmentation of natural objects from few training images and 5) a real-time place recognition system combining dimensionality reduction and Bayesian learning. The theoretical contributions of this thesis are demonstrated with a series of experiments in unstructured environments. In particular, the combination of recognition, representation and association algorithms is applied to the Simultaneous Localisation and Mapping problem (SLAM) to close large loops in outdoor trajectories, proving the benefits of the proposed methodology.

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Karlsson, Anders, and Jon Bjärkefur. "Simultaneous Localisation and Mapping of Indoor Environments Using a Stereo Camera and a Laser Camera." Thesis, Linköpings universitet, Institutionen för systemteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-64447.

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This thesis describes and investigates different approaches to indoor mapping and navigation. A system capable of mapping large indoor areas with a stereo camera and/or a laser camera mounted to e.g. a robot or a human is developed. The approaches investigated in this report are either based on Simultaneous Lo- calisation and Mapping (SLAM) techniques, e.g. Extended Kalman Filter-SLAM (EKF-SLAM) and Smoothing and Mapping (SAM), or registration techniques, e.g. Iterated Closest Point (ICP) and Normal Distributions Transform (NDT).In SLAM, it is demonstrated that the laser camera can contribute to the stereo camera by providing accurate distance estimates. By combining these sensors in EKF-SLAM, it is possible to obtain more accurate maps and trajectories compared to if the stereo camera is used alone.It is also demonstrated that by dividing the environment into smaller ones, i.e. submaps, it is possible to build large maps in close to linear time. A new approach to SLAM based on EKF-SLAM and SAM, called Submap Joining Smoothing and Mapping (SJSAM), is implemented to demonstrate this.NDT is also implemented and the objective is to register two point clouds from the laser camera to each other so that the relative motion can be obtained. The NDT implementation is compared to ICP and the results show that NDT performs better at estimating the angular difference between the point clouds.

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Wolf, Ryan Evan. "Stereo visual simultaneous localisation and mapping for an outdoor wheeled robot: a front-end study." Master's thesis, Faculty of Engineering and the Built Environment, 2019. http://hdl.handle.net/11427/31018.

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For many mobile robotic systems, navigating an environment is a crucial step in autonomy and Visual Simultaneous Localisation and Mapping (vSLAM) has seen increased effective usage in this capacity. However, vSLAM is strongly dependent on the context in which it is applied, often using heuristic and special cases to provide efficiency and robustness. It is thus crucial to identify the important parameters and factors regarding a particular context as this heavily influences the necessary algorithms, processes, and hardware required for the best results. In this body of work, a generic front-end stereo vSLAM pipeline is tested in the context of a small-scale outdoor wheeled robot that occupies less than 1m3 of volume. The scale of the vehicle constrained the available processing power, Field Of View (FOV), actuation systems, and image distortions present. A dataset was collected with a custom platform that consisted of a Point Grey Bumblebee (Discontinued) stereo camera and Nvidia Jetson TK1 processor. A stereo front-end feature tracking framework was described and evaluated both in simulation and experimentally where appropriate. It was found that scale adversely affected lighting conditions, FOV, baseline, and processing power available, all crucial factors to improve upon. The stereo constraint was effective for robustness criteria, but ineffective in terms of processing power and metric reconstruction. An overall absolute odometer error of 0.25-3m was produced on the dataset but was unable to run in real-time.

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Desrochers, Benoît. "Simultaneous localization and mapping in unstructured environments : a set-membership approach." Thesis, Brest, École nationale supérieure de techniques avancées Bretagne, 2018. http://www.theses.fr/2018ENTA0006/document.

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Cette thèse étudie le problème de la localisation et de la cartographie simultanée (SLAM), dans des environnements non structurés, c'est-à-dire, qui ne peuvent pas être décrits par des équations ou des formes géométriques. Ces types d'environnements sont souvent rencontrés dans le domaine sous-marin. Contrairement aux approches classiques, l'environnement n'est pas modélisé par une collection de descripteurs ou d'amers ponctuels, mais directement par des ensembles. Ces ensembles, appelés forme ou shape, sont associés à des caractéristiques physiques de l'environnement, comme par exemple, des textures, du relief ou, de manière plus symbolique, à l'espace libre autour du véhicule. D'un point de vue théorique, le problème du SLAM, basé sur des formes, est formalisé par un réseau de contraintes hybrides dont les variables sont des vecteurs de Rn et des sous-ensembles de Rn. De la même façon que l'incertitude sur une variable réelle est représentée par un intervalle de réels, l'incertitude sur les formes sera représentée par un intervalle de forme. La principale contribution de cette thèse est de proposer un formalisme, basé sur le calcul par intervalle, capable de calculer ces domaines. En application, les algorithmes développés ont été appliqués au problème du SLAM à partir de données bathymétriques recueillies par un véhicule sous-marin autonome (AUV)
This thesis deals with the simultaneous localization and mapping (SLAM) problem in unstructured environments, i.e. which cannot be described by geometrical features. This type of environment frequently occurs in an underwater context.Unlike classical approaches, the environment is not described by a collection of punctual features or landmarks, but directly by sets. These sets, called shapes, are associated with physical features such as the relief, some textures or, in a more symbolic way, the space free of obstacles that can be sensed around a robot. In a theoretical point of view, the SLAM problem is formalized as an hybrid constraint network where the variables are vectors and subsets of Rn. Whereas an uncertain real number is enclosed in an interval, an uncertain shape is enclosed in an interval of sets. The main contribution of this thesis is the introduction of a new formalism, based on interval analysis, able to deal with these domains. As an application, we illustrate our method on a SLAM problem based on bathymetric data acquired by an autonomous underwater vehicle (AUV)

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Dahlin, Alfred. "Simultaneous Localization and Mapping for an Unmanned Aerial Vehicle Using Radar and Radio Transmitters." Thesis, Linköpings universitet, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110645.

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The Global Positioning System (GPS) is a cornerstone in Unmanned Aerial Vehicle (UAV) navigation and is by far the most common way to obtain the position of a UAV. However, since there are many scenarios in which GPS measurements might not be available, the possibility of estimating the UAV position without using the GPS would greatly improve the overall robustness of the navigation. This thesis studies the possibility of instead using Simultaneous Localisation and Mapping (SLAM) in order to estimate the position of a UAV using an Inertial Measurement Unit (IMU) and the direction towards ground based radio transmitters without prior knowledge of their position. Simulations using appropriately generated data provides a feasibility analysis which shows promising results for position errors for outdoor trajectories over large areas, however with some issues regarding overall offset. The method seems to have potential but further studies are required using the measurements from a live flight, in order to determine the true performance.

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Hausler, Stephen D. "Appearance and viewpoint invariant visual place recognition using multi-scale and multi-modality systems." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/226953/1/Stephen_Hausler_Thesis.pdf.

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This thesis provides solutions to the problem of visual place recognition, that is, the ability for an autonomous system to recognise where it is in the world using images. The thesis shows how novel ensemble and multi-scale methods can be combined with modern artificial intelligence techniques to provide robust and reliable place recognition capabilities. It provides autonomous systems with enhanced positioning capabilities that can facilitate navigation even in challenging environmental conditions, such as at night or during adverse weather.

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Williams, Stefan Bernard. "Efficient Solutions to Autonomous Mapping and Navigation Problems." Thesis, The University of Sydney, 2001. http://hdl.handle.net/2123/809.

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This thesis deals with the Simultaneous Localisation and Mapping algorithm as it pertains to the deployment of mobile systems in unknown environments. Simultaneous Localisation and Mapping (SLAM) as defined in this thesis is the process of concurrently building up a map of the environment and using this map to obtain improved estimates of the location of the vehicle. In essence, the vehicle relies on its ability to extract useful navigation information from the data returned by its sensors. The vehicle typically starts at an unknown location with no a priori knowledge of landmark locations. From relative observations of landmarks, it simultaneously computes an estimate of vehicle location and an estimate of landmark locations. While continuing in motion, the vehicle builds a complete map of landmarks and uses these to provide continuous estimates of the vehicle location. The potential for this type of navigation system for autonomous systems operating in unknown environments is enormous. One significant obstacle on the road to the implementation and deployment of large scale SLAM algorithms is the computational effort required to maintain the correlation information between features in the map and between the features and the vehicle. Performing the update of the covariance matrix is of O(n3) for a straightforward implementation of the Kalman Filter. In the case of the SLAM algorithm, this complexity can be reduced to O(n2) given the sparse nature of typical observations. Even so, this implies that the computational effort will grow with the square of the number of features maintained in the map. For maps containing more than a few tens of features, this computational burden will quickly make the update intractable - especially if the observation rates are high. An effective map-management technique is therefore required in order to help manage this complexity. The major contributions of this thesis arise from the formulation of a new approach to the mapping of terrain features that provides improved computational efficiency in the SLAM algorithm. Rather than incorporating every observation directly into the global map of the environment, the Constrained Local Submap Filter (CLSF) relies on creating an independent, local submap of the features in the immediate vicinity of the vehicle. This local submap is then periodically fused into the global map of the environment. This representation is shown to reduce the computational complexity of maintaining the global map estimates as well as improving the data association process by allowing the association decisions to be deferred until an improved local picture of the environment is available. This approach also lends itself well to three natural extensions to the representation that are also outlined in the thesis. These include the prospect of deploying multi-vehicle SLAM, the Constrained Relative Submap Filter and a novel feature initialisation technique. Results of this work are presented both in simulation and using real data collected during deployment of a submersible vehicle equipped with scanning sonar.

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Frost, Duncan. "Long range monocular SLAM." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:af38cfa6-fc0a-48ab-b919-63c440ae8774.

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This thesis explores approaches to two problems in the frame-rate computation of a priori unknown 3D scene structure and camera pose using a single camera, or monocular simultaneous localisation and mapping. The thesis reflects two trends in vision in general and structure from motion in particular: (i) the move from directly recovered and towards learnt geometry; and (ii) the sparsification of otherwise dense direct methods. The first contributions mitigate scale drift. Beyond the inevitable accumulation of random error, monocular SLAM accumulates error via the depth/speed scaling ambiguity. Three solutions are investigated. The first detects objects of known class and size using fixed descriptors, and incorporates their measurements in the 3D map. Experiments using databases with ground truth show that metric accuracy can be restored over kilometre distances; and similar gains are made using a hand-held camera. Our second method avoids explicit feature choice, instead employing a deep convolutional neural network to yield depth priors. Relative depths are learnt well, but absolute depths less so, and recourse to database-wide scaling is investigated. The third approach uses a novel trained network to infer speed from imagery. The second part of the thesis develops sparsified direct methods for monocular SLAM. The first contribution is a novel camera tracker operating directly using affine image warping, but on patches around sparse corners. Camera pose is recovered with an accuracy at least equal to the state of the art, while requiring only half the computational time. The second introduces a least squares adjustment to sparsified direct map refinement, again using patches from sparse corners. The accuracy of its 3D structure estimation is compared with that from the widely used method of depth filtering. It is found empirically that the new method's accuracy is often higher than that of its filtering counterpart, but that the method is more troubled by occlusion.

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Skinner, John R. "Simulation for robot vision." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/227404/1/John_Skinner_Thesis.pdf.

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This thesis examined the effectiveness of using computer graphics technologies to create simulated data for vision-enabled robots. The research demonstrated that while the behaviour of a robot is greatly affected by what it is looking at, simulated scenes can produce position estimates similar to specific real locations. The findings show that robots need to be tested in a very wide range of different contexts to understand their performance, and simulation provides a cost-effective route to that evaluation.

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Vial, John Francis Stephen. "Conservative Sparsification for Efficient Approximate Estimation." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9907.

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Linear Gaussian systems often exhibit sparse structures. For systems which grow as a function of time, marginalisation of past states will eventually introduce extra non-zero elements into the information matrix of the Gaussian distribution. These extra non-zeros can lead to dense problems as these systems progress through time. This thesis proposes a method that can delete elements of the information matrix while maintaining guarantees about the conservativeness of the resulting estimate with a computational complexity that is a function of the connectivity of the graph rather than the problem dimension. This sparsification can be performed iteratively and minimises the Kullback Leibler Divergence (KLD) between the original and approximate distributions. This new technique is called Conservative Sparsification (CS). For large sparse graphs employing a Junction Tree (JT) for estimation, efficiency is related to the size of the largest clique. Conservative Sparsification can be applied to clique splitting in JTs, enabling approximate and efficient estimation in JTs with the same conservative guarantees as CS for information matrices. In distributed estimation scenarios which use JTs, CS can be performed in parallel and asynchronously on JT cliques. This approach usually results in a larger KLD compared with the optimal CS approach, but an upper bound on this increased divergence can be calculated with information locally available to each clique. This work has applications in large scale distributed linear estimation problems where the size of the problem or communication overheads make optimal linear estimation difficult.

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Larnaout, Dorra. "Localisation d'un véhicule à l'aide d'un SLAM visuel contraint." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2014. http://tel.archives-ouvertes.fr/tel-01038016.

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Pour se localiser en ville, la majorité des solutions commercialisées se base sur les systèmes GPS. Même si ces systèmes offrent une précision suffisante hors agglomération, celle-ci se dégradent considérablement en villes à cause des phénomènes connus sous le nom du canyon urbain (i.e. réflexion du signal GPS sur les façades des bâtiments). Pour pallier ce problème, les solutions basées sur un SLAM visuel (Simultaneous Localization And Mapping) semblent une alternative prometteuse. En plus de l'estimation des six degrés de liberté de la caméra mobile, il fournit une carte 3D de la scène observée. Toutefois, la localisation assurée par le SLAM visuel n'est pas géo-référencée et présente souvent des dérives (e.g. mauvaise estimation du facteur d'échelle, accumulation des erreurs). Pour faire face à ces limitations et afin de proposer une solution facile à déployer, nous avons étudié la possibilité d'intégrer au SLAM des informations supplémentaires qui pourraient contraindre l'ensemble de la reconstruction fournie. Ces dernières doivent alors être peu couteuses et disponibles en milieux urbains denses et péri-urbains. C'est pour cette raison que nous avons choisi d'exploiter les contraintes fournies par un GPS standard et celles apportées par des modèles issus des Systèmes d'Information Géographique, plus précisément : des modèles 3D des bâtiments et des modèles d'élévation de terrain. La principale contribution de ces travaux réside en l'intégration de ces contraintes au sein de l'ajustement de faisceaux (i.e. processus d'optimisation du SLAM). Ceci n'est pas trivial étant donné que combiner des contraintes agissant sur la trajectoire de la caméra et la reconstruction 3D peut entrainer des problèmes de convergences, en particulier lorsque les informations exploitées ont des incertitudes variées, voire même des données biaisées ou aberrantes (e.g. Pour les mesures du GPS). Différentes solutions Larnaout et al. (2012, 2013a,b,c) permettant de combiner plusieurs de ces contraintes simultanément tout en limitant les problèmes de convergence ont été développées. Les solutions proposées ont été validées sur des séquences de synthèse et d'autres réelles de plusieurs kilomètres enregistrées dans des conditions de circulation normale. Les résultats obtenus montrent que la précision atteinte au niveau de l'estimation des six degrés de liberté de la caméra permet d'assurer des nouvelles applications d'aide à la navigation par le biais de la Réalité Augmentée. En plus de leur précision, nos approches ont l'avantage d'être rapides, peu couteuses et faciles à déployer (ne nécessitant pas un matériel sophistiqué).

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Botterill, Tom. "Visual navigation for mobile robots using the Bag-of-Words algorithm." Thesis, University of Canterbury. Computer Science and Software Engineering, 2011. http://hdl.handle.net/10092/5511.

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Robust long-term positioning for autonomous mobile robots is essential for many applications. In many environments this task is challenging, as errors accumulate in the robot’s position estimate over time. The robot must also build a map so that these errors can be corrected when mapped regions are re-visited; this is known as Simultaneous Localisation and Mapping, or SLAM. Successful SLAM schemes have been demonstrated which accurately map tracks of tens of kilometres, however these schemes rely on expensive sensors such as laser scanners and inertial measurement units. A more attractive, low-cost sensor is a digital camera, which captures images that can be used to recognise where the robot is, and to incrementally position the robot as it moves. SLAM using a single camera is challenging however, and many contemporary schemes suffer complete failure in dynamic or featureless environments, or during erratic camera motion. An additional problem, known as scale drift, is that cameras do not directly measure the scale of the environment, and errors in relative scale accumulate over time, introducing errors into the robot’s speed and position estimates. Key to a successful visual SLAM system is the ability to continue operation despite these difficulties, and to recover from positioning failure when it occurs. This thesis describes the development of such a scheme, which is known as BoWSLAM. BoWSLAM enables a robot to reliably navigate and map previously unknown environments, in real-time, using only a single camera. In order to position a camera in visually challenging environments, BoWSLAM combines contemporary visual SLAM techniques with four new components. Firstly, a new Bag-of-Words (BoW) scheme is developed, which allows a robot to recognise places it has visited previously, without any prior knowledge of its environment. This BoW scheme is also used to select the best set of frames to reconstruct positions from, and to find efficient wide-baseline correspondences between many pairs of frames. Secondly, BaySAC, a new outlier- robust relative pose estimation scheme based on the popular RANSAC framework, is developed. BaySAC allows the efficient computation of multiple position hypotheses for each frame. Thirdly, a graph-based representation of these position hypotheses is proposed, which enables the selection of only reliable position estimates in the presence of gross outliers. Fourthly, as the robot explores, objects in the world are recognised and measured. These measurements enable scale drift to be corrected. BoWSLAM is demonstrated mapping a 25 minute 2.5km trajectory through a challenging and dynamic outdoor environment in real-time, and without any other sensor input; considerably further than previous single camera SLAM schemes.

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Melbouci, Kathia. "Contributions au RGBD-SLAM." Thesis, Université Clermont Auvergne‎ (2017-2020), 2017. http://www.theses.fr/2017CLFAC006/document.

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Pour assurer la navigation autonome d’un robot mobile, les traitements effectués pour sa localisation doivent être faits en ligne et doivent garantir une précision suffisante pour permettre au robot d’effectuer des tâches de haut niveau pour la navigation et l’évitement d’obstacles. Les auteurs de travaux basés sur le SLAM visuel (Simultaneous Localization And Mapping) tentent depuis quelques années de garantir le meilleur compromis rapidité/précision. La majorité des solutions SLAM visuel existantes sont basées sur une représentation éparse de l’environnement. En suivant des primitives visuelles sur plusieurs images, il est possible d’estimer la position 3D de ces primitives ainsi que les poses de la caméra. La communauté du SLAM visuel a concentré ses efforts sur l’augmentation du nombre de primitives visuelles suivies et sur l’ajustement de la carte 3D, afin d’améliorer l’estimation de la trajectoire de la caméra et les positions 3D des primitives. Cependant, la localisation par SLAM visuel présente souvent des dérives dues au cumul d’erreurs, et dans le cas du SLAM visuel monoculaire, la position de la caméra n’est connue qu’à un facteur d’échelle près. Ce dernier peut être fixé initialement mais dérive au cours du temps. Pour faire face à ces limitations, nous avons centré nos travaux de thèse sur la problématique suivante : intégrer des informations supplémentaires dans un algorithme de SLAM visuel monoculaire afin de mieux contraindre la trajectoire de la caméra et la reconstruction 3D. Ces contraintes ne doivent pas détériorer les performances calculatoires de l’algorithme initial et leur absence ne doit pas mettre l’algorithme en échec. C’est pour cela que nous avons choisi d’intégrer l’information de profondeur fournie par un capteur 3D (e.g. Microsoft Kinect) et des informations géométriques sur la structure de la scène. La première contribution de cette thèse est de modifier l’algorithme SLAM visuel monoculaire proposé par Mouragnon et al. (2006b) pour prendre en compte la mesure de profondeur fournie par un capteur 3D, en proposant particulièrement un ajustement de faisceaux qui combine, d’une manière simple, des informations visuelles et des informations de profondeur. La deuxième contribution est de proposer une nouvelle fonction de coût du même ajustement de faisceaux qui intègre, en plus des contraintes sur les profondeurs des points, des contraintes géométriques d’appartenance aux plans de la scène. Les solutions proposées ont été validées sur des séquences de synthèse et sur des séquences réelles, représentant des environnements variés. Ces solutions ont été comparées aux récentes méthodes de l’état de l’art. Les résultats obtenus montrent que les différentes contraintes développées permettent d’améliorer significativement la précision de la localisation du SLAM. De plus les solutions proposées sont faciles à déployer et peu couteuses en temps de calcul
To guarantee autonomous and safely navigation for a mobile robot, the processing achieved for its localization must be fast and accurate enough to enable the robot to perform high-level tasks for navigation and obstacle avoidance. The authors of Simultaneous Localization And Mapping (SLAM) based works, are trying since year, to ensure the speed/accuracy trade-off. Most existing works in the field of monocular (SLAM) has largely centered around sparse feature-based representations of the environment. By tracking salient image points across many frames of video, both the positions of the features and the motion of the camera can be inferred live. Within the visual SLAM community, there has been a focus on both increasing the number of features that can be tracked across an image and efficiently managing and adjusting this map of features in order to improve camera trajectory and feature location accuracy. However, visual SLAM suffers from some limitations. Indeed, with a single camera and without any assumptions or prior knowledge about the camera environment, rotation can be retrieved, but the translation is up to scale. Furthermore, visual monocular SLAM is an incremental process prone to small drifts in both pose measurement and scale, which when integrated over time, become increasingly significant over large distances. To cope with these limitations, we have centered our work around the following issues : integrate additional information into an existing monocular visual SLAM system, in order to constrain the camera localization and the mapping points. Provided that the high speed of the initial SLAM process is kept and the lack of these added constraints should not give rise to the failure of the process. For these last reasons, we have chosen to integrate the depth information provided by a 3D sensor (e.g. Microsoft Kinect) and geometric information about scene structure. The primary contribution of this work consists of modifying the SLAM algorithm proposed by Mouragnon et al. (2006b) to take into account the depth measurement provided by a 3D sensor. This consists of several rather straightforward changes, but also on a way to combine the depth and visual data in the bundle adjustment process. The second contribution is to propose a solution that uses, in addition to the depth and visual data, the constraints lying on points belonging to the plans of the scene. The proposed solutions have been validated on a synthetic sequences as well as on a real sequences, which depict various environments. These solutions have been compared to the state of art methods. The performances obtained with the previous solutions demonstrate that the additional constraints developed, improves significantly the accuracy and the robustness of the SLAM localization. Furthermore, these solutions are easy to roll out and not much time consuming

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Loesch, Angélique. "Localisation d'objets 3D industriels à l'aide d'un algorithme de SLAM contraint au modèle." Thesis, Université Clermont Auvergne‎ (2017-2020), 2017. http://www.theses.fr/2017CLFAC059/document.

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Un besoin applicatif existe en terme de localisation 3D d’objets par vision. Cette technologie devient en effet de plus en plus populaire dans le milieu industriel où elle peut être utile lors de contrôle qualité, de robotisation de tâches ou encore d’aide à la maintenance par Réalité Augmentée. Néanmoins, le déploiement de telles applications est actuellement limité en raison de la difficulté à allier qualité de localisation, facilité de mise en oeuvre et généricité de la solution. En effet, la majorité des solutions implique : soit des étapes de mise en oeuvre complexes comme avec l’installation de capteurs de mouvement ou une préparation supervisée du modèle CAO; soit un manque de précision de la localisation dans le cadre de certaines applications nécessitant de prendre en compte des mouvements de fortes amplitudes de la caméra (provoquant du flou de bouger et des tremblements dans le flux vidéo) ainsi que des occultations partielles ou totales de l’objet ; soit enfin une restriction sur la nature de l’objet, celui-ci devant être texturé, de petite taille ou encore polyédrique pour avoir une bonne localisation. La plupart des solutions de localisation existantes correspondent à des approches de suivi basé modèle. Cette méthode consiste à estimer la pose relative entre la caméra et l’objet d’intérêt par mises en correspondance de primitives 3D extraites du modèle avec des primitives 2D extraites d’images d’un flux vidéo. Pour autant, cette approche atteint ses limites lorsque l’objet est difficilement observable dans l’image.Afin d’améliorer la localisation lorsque l’application concerne un objet fixe, de récentes solutions se sont appuyées en complément des primitives du modèle, sur des primitives de l’environnement reconstruites au cours du processus de localisation. Ces approches combinent algorithmes de SLAM (Simultaneous Localization And Mapping) et de suivi d’objet basé contours en utilisant les informations du modèle comme contrainte dans le processus d’optimisation du SLAM. Pour cela, un terme d’erreur est ajouté à la fonction de coût classique.Celui-ci mesure l’erreur de re-projection entre des primitives 3D issues des arêtes franches du modèle et les points de contour 2D dans l’image qui leur sont associés. L’ajout de cette contrainte permet d’exprimer la localisation du SLAM dans le repère de l’objet d’intérêt tout en réduisant sa dérive. Les solutions de SLAM contraint au modèle n’exploitant cependant que les contours francs du modèle, ne sont pas génériques et ne permettent de localiser que des objets polyédriques. De plus, l’ajout de cette contrainte entraîne une forte augmentation de la consommation mémoire, les images de contours nécessaires à l’étape de mise en correspondance devant être conservées.Les travaux présentés dans ce mémoire de thèse visent à fournir une solution répondant simultanément à l’ensemble des besoins concernant la facilité de déploiement, la qualité de localisation et la généricité sur la nature des objets suivis. Aussi, notre solution basée sur un algorithme de SLAM visuel contraint basé images clés, se restreint-elle au seul usage d’une caméra couleur, les caméras RGBD impliquant généralement une limite sur le volume, la nature réflective ou absorbante de l’objet, et sur la luminosité de son environnement. Cette étude est en outre restreinte à la seule exploitation de modèles 3D géométrique non texturés, les textures pouvant difficilement être considérées comme stables dans le temps (usure, taches...) et pouvant varier pour un même objet manufacturé. De plus, les modèles à base de nuages de descripteurs locaux ou les modèles surfaciques texturés sont actuellement des données peu disponibles dans l’industrie. Enfin, nous faisons le choix d’estimer la pose de la caméra de manière géométrique et non par apprentissage. Le suivi d’objets à l’aide d’apprentissage automatique est en effet encore difficilement exploitable en milieu industriel. (...)
In the industry domain, applications such as quality control, automation of complex tasks or maintenance support with Augmented Reality (AR) could greatly benefit from visual tracking of 3D objects. However, this technology is under-exploited due to the difficulty of providing deployment easiness, localization quality and genericity simultaneously. Most existing solutions indeed involve a complex or an expensive deployment of motion capture sensors, or require human supervision to simplify the 3D model. And finally, most tracking solutions are restricted to textured or polyhedral objects to achieved an accurate camera pose estimation.Tracking any object is a challenging task due to the large variety of object forms and appearances. Industrial objects may indeed have sharp edges, or occluding contours that correspond to non-static and view-point dependent edges. They may also be textured or textureless. Moreover, some applications require to take large amplitude motions as well as object occlusions into account, tasks that are not always dealt with common model-based tracking methods. These approaches indeed exploit 3D features extracted from a model, that are matched with 2D features in the image of a video-stream. However the accuracy and robustness of the camera localization depend on the visibility of the object as well as on the motion of the camera. To better constrain the localization when the object is static, recent solutions rely on environment features that are reconstructed online, in addition to the model ones. These approaches combine SLAM (Simultaneous Localization And Mapping) and model-based tracking solutions by using constraints from the 3D model of the object of interest. Constraining SLAM algorithms with a 3D model results in a drift free localization. However, such approaches are not generic since they are only adapted for textured or polyhedral objects. Furthermore, using the 3D model to constrain the optimization process may generate high memory consumption,and limit the optimization to a temporal window of few cameras. In this thesis, we propose a solution that fulfills the requirements concerning deployment easiness, localization quality and genericity. This solution, based on a visual key-frame-based constrained SLAM, only exploits an RGB camera and a geometric CAD model of the static object of interest. An RGB camera is indeed preferred over an RGBD sensor, since the latter imposes limits on the volume, the reflectiveness or the absorptiveness of the object, and the lighting conditions. A geometric CAD model is also preferred over a textured model since textures may hardly be considered as stable in time (deterioration, marks,...) and may vary for one manufactured object. Furthermore, textured CAD models are currently not widely spread. Contrarily to previous methods, the presented approach deals with polyhedral and curved objects by extracting dynamically 3D contour points from a model rendered on GPU. This extraction is integrated as a structure constraint into the constrained bundle adjustment of a SLAM algorithm. Moreover we propose different formalisms of this constraint to reduce the memory consumption of the optimization process. These formalisms correspond to hybrid structure/trajectory constraints, that uses output camera poses of a model-based tracker. These formalisms take into account the structure information given by the 3D model while relying on the formalism of trajectory constraints. The proposed solution is real-time, accurate and robust to occlusion or sudden motion. It has been evaluated on synthetic and real sequences of different kind of objects. The results show that the accuracy achieved on the camera trajectory is sufficient to ensure a solution perfectly adapted for high-quality Augmented Reality experiences for the industry

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32

Dia, Roxana. "Towards Environment Perception using Integer Arithmetic for Embedded Application." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM038.

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Le principal inconvénient de l'utilisation de représentations basées sur la grille pour SLAM et pour la localisation globale est la complexité de calcul exponentielle requise en termes de taille de grille (de la carte et des cartes de pose). La taille de grille requise pour modéliser l'environnement entourant un robot ou un véhicule peut être de l'ordre de milliers de millions de cellules. Par exemple, un espace 2D de forme carrée de taille 100 m × 100 m, avec une taille de cellule de 10 cm est modélisé avec une grille de 1 million de cellules. Si nous incluons une hauteur de 2 m pour représenter la troisième dimension, 20 millions de cellules sont nécessaires. Par conséquent, les approches SLAM classiques basées sur une grille et de localisation globale nécessitent une unité de calcul parallèle afin de répondre à la latence imposée par les normes de sécurité. Un tel calcul est généralement effectué sur des postes de travail intégrant des unités de traitement graphique (GPU) et / ou des processeurs haut de gamme. Cependant, les véhicules autonomes ne peuvent pas gérer de telles plateformes pour des raisons de coût et de problèmes de certification. De plus, ces plates-formes nécessitent une consommation d'énergie élevée qui ne peut pas correspondre à la source d'énergie limitée disponible dans certains robots. Les plates-formes matérielles intégrées sont couramment utilisées comme solution alternative dans les applications automobiles. Ces plateformes répondent aux contraintes de faible coût, de faible puissance et de petit espace. De plus, certains d'entre eux sont certifiés automobiles1, suivant la norme ISO26262. Cependant, la plupart d'entre eux ne sont pas équipés d'une unité à virgule flottante, ce qui limite les performances de calcul.L'équipe du projet sigma-fusion du laboratoire LIALP du CEA-Leti a développé une méthode de perception basée sur des nombres entiers adaptée aux appareils embarqués. Cette méthode permet de construire une grille d'occupation via la fusion bayésienne en utilisant uniquement l'arithmétique entière, d'où son "embarquabilité" sur des plateformes informatiques embarquées, sans unité à virgule flottante. Ceci constitue la contribution majeure de la thèse de doctorat de Tiana Rakotovao [Rakotovao Andriamahefa 2017].L'objectif de la présente thèse est d'étendre le cadre de perception des nombres entiers au SLAM et aux problèmes de localisation globale, offrant ainsi des solutions «embarquables» sur les systèmes embarqués
The main drawback of using grid-based representations for SLAM and for global localization is the required exponential computational complexity in terms of the grid size (of the map and the pose maps). The required grid size for modeling the environment surrounding a robot or of a vehicle can be in the order of thousands of millions of cells. For instance, a 2D square-shape space of size 100m × 100m, with a cell size of 10cm is modelled with a grid of 1 million cells. If we include a 2m of height to represent the third dimension, 20 millions of cells are required. Consequently, classical grid-based SLAM and global localization approaches require a parallel computing unit in order to meet the latency imposed by safety standards. Such a computation is usually done over workstations embedding Graphical Processing Units (GPUs) and/or a high-end CPUs. However, autonomous vehicles cannot handle such platforms for cost reason, and certification issues. Also, these platforms require a high power consumption that cannot fit within the limited source of energy available in some robots. Embedded hardware platforms are com- monly used as an alternative solution in automotive applications. These platforms meet the low-cost, low-power and small-space constraints. Moreover, some of them are automotive certified1, following the ISO26262 standard. However, most of them are not equipped with a floating-point unit, which limits the computational performance.The sigma-fusion project team in the LIALP laboratory at CEA-Leti has developed an integer-based perception method suitable for embedded devices. This method builds an occupancy grid via Bayesian fusion using integer arithmetic only, thus its "embeddability" on embedded computing platforms, without floating-point unit. This constitutes the major contribution of the PhD thesis of Tiana Rakotovao [Rakotovao Andriamahefa 2017].The objective of the present PhD thesis is to extend the integer perception framework to SLAM and global localization problems, thus offering solutions “em- beddable” on embedded systems

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Lothe, Pierre. "Localication et cartographie simultanées par vision monoculaire contraintes par un SIG : application à la géolocalisation d'un véhicule." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2010. http://tel.archives-ouvertes.fr/tel-00625652.

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Les travaux réalisés au cours de cette thèse s'inscrivent dans les problématiques de localisation d'un véhicule par vision. Nous nous plaçons en particulier dans le cas de parcours sur de longues distances, c'est à dire plusieurs kilomètres. Les méthodes actuelles de localisation et cartographie simultanées souffrent de problèmes de dérives qui les rendent difficilement exploitables après plusieurs centaines de mètres. Nous proposons dans ce mémoire de pallier ces limites en exploitant une connaissance à priori sur la géométrie de l'environnement parcouru.Cette information est extraite d'un Système d'Information Géographique. En particulier, les travaux réalisés se basent sur les modèles 3D des bâtiments des villes et sur une carte de la route.Dans la première partie de ce mémoire, nous proposons une approche permettant de corriger hors ligne une reconstruction SLAM en exploitant la connaissance d'un modèle 3D simple de l'environnement. Cette correction s'applique en deux étapes. En premier lieu, un recalage non-rigide entre le nuage de points reconstruit et le modèle 3D est effectué de sorte à retrouver la cohérence globale de la reconstruction. Dans le but de raffiner le nuage de points obtenu, un ajustement de faisceaux contraint par le SIG est alors effectué sur l'ensemble de la reconstruction.La particularité de cet ajustement de faisceaux est qu'il prend implicitement en compte les contraintes géométriques apportées par le modèle 3D. La reconstruction ainsi corrigée est alors utilisée en tant que base de données pour la relocalisation en ligne d'une caméra mobile. La précision de relocalisation obtenue est en particulier suffisante pour les applications de réalité augmentée.Dans la deuxième partie de ce mémoire, nous détaillons une solution permettant de corriger en ligne la reconstruction SLAM. Pour cela, les contraintes géométriques apportées par le SIG sont exploitées au fur et à mesure de la trajectoire du véhicule. Nous montrons tout d'abord que la connaissance de la position relative de la caméra par rapport à la route permet de corriger de façon robuste la dérive de facteur d'échelle. De plus, lorsque les contraintes géométriques sont suffisantes, la reconstruction SLAM réalisée jusqu'à l'instant courant est recalée sur le SIG.Cela permet de corriger ponctuellement la dérive observée sur la position courante de la caméra.Le processus complet permet dès lors de localiser le véhicule avec une précision semblable à celle d'un système GPS sur des trajectoires de plusieurs kilomètres.Les deux méthodes proposées ont été testées à la fois sur des séquences de synthèse et réelles. Des résultats qualitatifs et quantitatifs sont présentés tout au long de ce mémoire.

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Abouzahir, Mohamed. "Algorithmes SLAM : Vers une implémentation embarquée." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS058/document.

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La navigation autonome est un axe de recherche principal dans le domaine de la robotique mobile. Dans ce contexte, le robot doit disposer des algorithmes qui lui permettent d’évoluer de manière autonome dans des environnements complexes et inconnus. Les algorithmes de SLAM permettent à un robot de cartographier son environnement tout en se localisant dans l’espace. Les algorithmes SLAM sont de plus en plus performants, mais aucune implémentation matérielle ou architecturale complète n’a eu. Une telle implantation d’architecture doit prendre en considération la consommation d’énergie, l’embarquabilité et la puissance de calcul. Ce travail scientifique vise à évaluer des systèmes embarqués impliquant de la localisation ou reconstruction de scène. La méthodologie adoptera une approche A3 (Adéquation Algorithme Architecture) pour améliorer l’efficacité de l’implantation des algorithmes plus particulièrement pour des systèmes à fortes contraintes. Le système SLAM embarqué doit disposer d’une architecture électronique et logicielle permettant d’assurer la production d’information pertinentes à partir de données capteurs, tout en assurant la localisation de l’embarquant dans son environnement. L’objectif est donc de définir, pour un algorithme choisi, un modèle d’architecture répondant aux contraintes de l’embarqué. Les premiers travaux de cette thèse ont consisté à explorer les différentes approches algorithmiques permettant la résolution du problème de SLAM. Une étude plus approfondie de ces algorithmes est réalisée. Ceci nous a permet d’évaluer quatre algorithmes de différente nature : FastSLAM2.0, ORB SLAM, RatSLAM et le SLAM linéaire. Ces algorithmes ont été ensuite évalués sur plusieurs architectures pour l’embarqué afin d’étudier leur portabilité sur des systèmes de faible consommation énergétique et de ressources limitées. La comparaison prend en compte les temps d’exécutions et la consistance des résultats. Après avoir analysé profondément les évaluations temporelles de chaque algorithme, le FastSLAM2.0 est finalement choisi, pour un compromis temps d’exécution-consistance de résultat de localisation, comme candidat pour une étude plus approfondie sur une architecture hétérogène embarquée. La second partie de cette thèse est consacré à l’étude d’un système embarqué implémentant le FastSLAM2.0 monoculaire dédié aux environnements larges. Une réécriture algorithmique du FastSLAM2.0 a été nécessaire afin de l’adapter au mieux aux contraintes imposées par les environnements de grande échelle. Dans une démarche A3, le FastSLAM2.0 a été implanté sur une architecture hétérogène CPU-GPU. Grâce à un partitionnement efficace, un facteur d’accélération global de l’ordre de 22 a été obtenu sur une architecture récente dédiée pour l’embarqué. La nature du traitement de l’algorithme FastSLAM2.0 pouvait bénéficier d’une architecture fortement parallèle. Une deuxième instance matérielle basée sur une architecture programmable FPGA est proposée. L’implantation a été réalisée en utilisant des outils de synthèse de haut-niveau afin de réduire le temps de développement. Une comparaison des résultats d’implantation sur cette architecture matérielle par rapport à des architectures à base de GPU a été réalisée. Les gains obtenus sont conséquent, même par rapport aux GPU haut-de-gamme avec un grand nombre de cœurs. Le système résultant peut cartographier des environnements larges tout en garantissant le compromis entre la consistance des résultats de localisation et le temps réel. L’utilisation de plusieurs calculateurs implique d’utiliser des moyens d’échanges de données entre ces derniers. Cela passe par des couplages forts. Ces travaux de thèse ont permis de mettre en avant l’intérêt des architectures hétérogènes parallèles pour le portage des algorithmes SLAM. Les architectures hétérogènes à base de FPGA peuvent particulièrement devenir des candidats potentiels pour porter des algorithmes complexes traitant des données massives
Autonomous navigation is a main axis of research in the field of mobile robotics. In this context, the robot must have an algorithm that allow the robot to move autonomously in a complex and unfamiliar environments. Mapping in advance by a human operator is a tedious and time consuming task. On the other hand, it is not always reliable, especially when the structure of the environment changes. SLAM algorithms allow a robot to map its environment while localizing it in the space.SLAM algorithms are becoming more efficient, but there is no full hardware or architectural implementation that has taken place . Such implantation of architecture must take into account the energy consumption, the embeddability and computing power. This scientific work aims to evaluate the embedded systems implementing locatization and scene reconstruction (SLAM). The methodology will adopt an approach AAM ( Algorithm Architecture Matching) to improve the efficiency of the implementation of algorithms especially for systems with high constaints. SLAM embedded system must have an electronic and software architecture to ensure the production of relevant data from sensor information, while ensuring the localization of the robot in its environment. Therefore, the objective is to define, for a chosen algorithm, an architecture model that meets the constraints of embedded systems. The first work of this thesis was to explore the different algorithmic approaches for solving the SLAM problem. Further study of these algorithms is performed. This allows us to evaluate four different kinds of algorithms: FastSLAM2.0, ORB SLAM, SLAM RatSLAM and linear. These algorithms were then evaluated on multiple architectures for embedded systems to study their portability on energy low consumption systems and limited resources. The comparison takes into account the time of execution and consistency of results. After having deeply analyzed the temporal evaluations for each algorithm, the FastSLAM2.0 was finally chosen for its compromise performance-consistency of localization result and execution time, as a candidate for further study on an embedded heterogeneous architecture. The second part of this thesis is devoted to the study of an embedded implementing of the monocular FastSLAM2.0 which is dedicated to large scale environments. An algorithmic modification of the FastSLAM2.0 was necessary in order to better adapt it to the constraints imposed by the largescale environments. The resulting system is designed around a parallel multi-core architecture. Using an algorithm architecture matching approach, the FastSLAM2.0 was implemeted on a heterogeneous CPU-GPU architecture. Uisng an effective algorithme partitioning, an overall acceleration factor o about 22 was obtained on a recent dedicated architecture for embedded systems. The nature of the execution of FastSLAM2.0 algorithm could benefit from a highly parallel architecture. A second instance hardware based on programmable FPGA architecture is proposed. The implantation was performed using high-level synthesis tools to reduce development time. A comparison of the results of implementation on the hardware architecture compared to GPU-based architectures was realized. The gains obtained are promising, even compared to a high-end GPU that currently have a large number of cores. The resulting system can map a large environments while maintainingthe balance between the consistency of the localization results and real time performance. Using multiple calculators involves the use of a means of data exchange between them. This requires strong coupling (communication bus and shared memory). This thesis work has put forward the interests of parallel heterogeneous architectures (multicore, GPU) for embedding the SLAM algorithms. The FPGA-based heterogeneous architectures can particularly become potential candidatesto bring complex algorithms dealing with massive data

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Bacca, Cortés Eval Bladimir. "Appearance-based mapping and localization using feature stability histograms for mobile robot navigation." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/83589.

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This work proposes an appearance-based SLAM method whose main contribution is the Feature Stability Histogram (FSH). The FSH is built using a voting schema, if the feature is re-observed, it will be promoted; otherwise it progressively decreases its corresponding FSH value. The FSH is based on the human memory model to deal with changing environments and long-term SLAM. This model introduces concepts of Short-Term memory (STM), which retains information long enough to use it, and Long-Term memory (LTM), which retains information for longer periods of time. If the entries in the STM are rehearsed, they become part of the LTM (i.e. they become more stable). However, this work proposes a different memory model, allowing to any input be part of the STM or LTM considering the input strength. The most stable features are only used for SLAM. This innovative feature management approach is able to cope with changing environments, and long-term SLAM.
Este trabajo propone un método de SLAM basado en apariencia cuya principal contribución es el Histograma de Estabilidad de Características (FSH). El FSH es construido por votación, si una característica es re-observada, ésta será promovida; de lo contrario su valor FSH progresivamente es reducido. El FSH es basado en el modelo de memoria humana para ocuparse de ambientes cambiantes y SLAM a largo término. Este modelo introduce conceptos como memoria a corto plazo (STM) y largo plazo (LTM), las cuales retienen información por cortos y largos periodos de tiempo. Si una entrada a la STM es reforzada, ésta hará parte de la LTM (i.e. es más estable). Sin embargo, este trabajo propone un modelo de memoria diferente, permitiendo a cualquier entrada ser parte de la STM o LTM considerando su intensidad. Las características más estables son solamente usadas en SLAM. Esta innovadora estrategia de manejo de características es capaz de hacer frente a ambientes cambiantes y SLAM de largo término.

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Tamaazousti, Mohamed. "L'ajustement de faisceaux contraint comme cadre d'unification des méthodes de localisation : application à la réalité augmentée sur des objets 3D." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2013. http://tel.archives-ouvertes.fr/tel-00881206.

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Les travaux réalisés au cours de cette thèse s'inscrivent dans la problématique de localisation en temps réel d'une caméra par vision monoculaire. Dans la littérature, il existe différentes méthodes qui peuvent être classées en trois catégories. La première catégorie de méthodes considère une caméra évoluant dans un environnement complètement inconnu (SLAM). Cette méthode réalise une reconstruction enligne de primitives observées dans des images d'une séquence vidéo et utilise cette reconstruction pour localiser la caméra. Les deux autres permettent une localisation par rapport à un objet 3D de la scène en s'appuyant sur la connaissance, a priori, d'un modèle de cet objet (suivi basé modèle). L'une utilise uniquement l'information du modèle 3D de l'objet pour localiser la caméra, l'autre peut être considérée comme l'intermédiaire entre le SLAM et le suivi basé modèle. Cette dernière méthode consiste à localiser une caméra par rapport à un objet en utilisant, d'une part, le modèle de ce dernier et d'autre part, une reconstruction en ligne des primitives de l'objet d'intérêt. Cette reconstruction peut être assimilée à une mise à jour du modèle initial (suivi basé modèle avec mise à jour). Chacune de ces méthodes possède des avantages et des inconvénients. Dans le cadre de ces travaux de thèse, nous proposons une solution unifiant l'ensemble de ces méthodes de localisation dans un unique cadre désigné sous le terme de SLAM contraint. Cette solution, qui unifie ces différentes méthodes, permet de tirer profit de leurs avantages tout en limitant leurs inconvénients respectifs. En particulier, nous considérons que la caméra évolue dans un environnement partiellement connu, c'est-à-dire pour lequel un modèle (géométrique ou photométrique) 3D d'un objet statique de la scène est disponible. L'objectif est alors d'estimer de manière précise la pose de la caméra par rapport à cet objet 3D. L'information absolue issue du modèle 3D de l'objet d'intérêt est utilisée pour améliorer la localisation de type SLAM en incluant cette information additionnelle directement dans le processus d'ajustement de faisceaux. Afin de pouvoir gérer un large panel d'objets 3D et de scènes, plusieurs types de contraintes sont proposées dans ce mémoire. Ces différentes contraintes sont regroupées en deux approches. La première permet d'unifier les méthodes SLAM et de suivi basé modèle, en contraignant le déplacement de la caméra via la projection de primitives existantes extraites du modèle 3D dans les images. La seconde unifie les méthodes SLAM et de suivi basé modèle avec mise à jour en contraignant les primitives reconstruites par le SLAM à appartenir à la surface du modèle (unification SLAM et mise à jour du modèle). Les avantages de ces différents ajustements de faisceaux contraints, en terme de précision, de stabilité de recalage et de robustesse aux occultations, sont démontrés sur un grand nombre de données de synthèse et de données réelles. Des applications temps réel de réalité augmentée sont également présentées sur différents types d'objets 3D. Ces travaux ont fait l'objet de 4 publications internationales, de 2 publications nationales et d'un dépôt de brevet.

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37

Gérossier, Franck. "Localisation et cartographie simultanées en environnement extérieur à partir de données issues d'un radar panoramique hyperfréquence." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2012. http://tel.archives-ouvertes.fr/tel-00864181.

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Le SLAM, " Simultaneous Localisation And Mapping ", représente à l'heure actuelle l'une des principales thématiques investiguées dans le domaine des robots mobiles autonomes. Il permet, à l'aide de capteurs extéroceptifs (laser, caméra, radar, etc.) et proprioceptifs (odomètre, gyromètre, etc.), de trouver l'orientation et la localisation d'un robot dans un environnement extérieur vaste, inconnu ou modifié, avec la possibilité de créer une carte au fur et à mesure des déplacements du véhicule. Les travaux de thèse décrits dans ce manuscrit s'intègrent dans ce courant de recherche. Ils visent à développer un SLAM innovant qui utilise un radar à modulation de fréquence continue " FMCW " comme capteur extéroceptif. Ce capteur est insensible aux conditions climatiques et possède une portée de détection importante. Néanmoins, c'est un capteur tournant qui, dans une utilisation mobile, va fournir des données corrompues par le déplacement du véhicule. Pour mener à bien ces travaux, nous avons proposés différentes contributions : une correction de la distorsion par l'utilisation de capteurs proprioceptifs ; le développement d'une technique de localisation et cartographie simultanées nommée RS-SLAM-FMT qui effectue un scan matching sur les observations et utilise un algorithme estimatif de type EKF-SLAM ; l'utilisation, pour la première fois en SLAM, de la mise en correspondance par Transformée de Fourier-Mellin pour réaliser l'opération de scan matching ; la création d'un outil expérimental pour déterminer la matrice de covariance associée aux observations ; des tests de robustesse de l'algorithme dans des conditions d'utilisation réelles : dans des zones avec un faible nombre de points d'intérêts, sur des parcours effectués à vitesse élevée, dans des environnements péri-urbains avec une forte densité d'objets mobiles ; la réalisation d'une application temps réel pour le test du procédé sur un véhicule d'exploration qui se déplace dans un environnement extérieur vaste.

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Dujardin, Aymeric. "Détection d’obstacles par stéréovision en environnement non structuré." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMIR09.

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Les robots et véhicules autonomes représentent le futur des modes de déplacements et de production. Les enjeux de l’avenir reposent sur la robustesse de leurs perceptions et flexibilité face aux environnements changeant et situations inattendues. Les capteurs stéréoscopiques sont des capteurs passifs qui permettent d'obtenir à la fois image et information 3D de la scène à la manière de la vision humaine. Dans ces travaux nous avons développé un système de localisation, par odométrie visuelle permettant de déterminer la position dans l'espace du capteur de façon efficace et performante en tirant partie de la carte de profondeur dense mais également associé à un système de SLAM, rendant la localisation robuste aux perturbations et aux décalages potentiels. Nous avons également développé plusieurs solutions de cartographie et interprétation d’obstacles, à la fois pour le véhicule aérien et terrestre. Ces travaux sont en partie intégrés dans des produits commerciaux
Autonomous vehicles and robots represent the future of transportation and production industries. The challenge ahead will come from the robustness of perception and flexibility from unexpected situations and changing environments. Stereoscopic cameras are passive sensors that provide color images and depth information of the scene by correlating 2 images like the human vision. In this work, we developed a localization system, by visual odometry that can determine efficiently the position in space of the sensor by exploiting the dense depth map. It is also combined with a SLAM system that enables robust localization against disturbances and potentials drifts. Additionally, we developed a few mapping and obstacles detections solutions, both for aerial and terrestrial vehicles. These algorithms are now partly integrated into commercial products

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Csorba, Michael. "Simultaneous localisation and map building." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244562.

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Naghi, Nour. "Simultaneous Localization and Mapping Technologies." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17852/.

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Il problema dello SLAM (Simultaneous Localization And Mapping) consiste nel mappare un ambiente sconosciuto per mezzo di un dispositivo che si muove al suo interno, mentre si effettua la localizzazione di quest'ultimo. All'interno di questa tesi viene analizzato il problema dello SLAM e le differenze che lo contraddistinguono dai problemi di mapping e di localizzazione trattati separatamente. In seguito, si effettua una analisi dei principali algoritmi impiegati al giorno d'oggi per la sua risoluzione, ovvero i filtri estesi di Kalman e i particle filter. Si analizzano poi le diverse tecnologie implementative esistenti, tra le quali figurano sistemi SONAR, sistemi LASER, sistemi di visione e sistemi RADAR; questi ultimi, allo stato dell'arte, impiegano onde millimetriche (mmW) e a banda larga (UWB), ma anche tecnologie radio già affermate, fra le quali il Wi-Fi. Infine, vengono effettuate delle simulazioni di tecnologie basate su sistema di visione e su sistema LASER, con l'ausilio di due pacchetti open source di MATLAB. Successivamente, il pacchetto progettato per sistemi LASER è stato modificato al fine di simulare una tecnologia SLAM basata su segnali Wi-Fi. L'utilizzo di tecnologie a basso costo e ampiamente diffuse come il Wi-Fi apre alla possibilità, in un prossimo futuro, di effettuare localizzazione indoor a basso costo, sfruttando l'infrastruttura esistente, mediante un semplice smartphone. Più in prospettiva, l'avvento della tecnologia ad onde millimetriche (5G) consentirà di raggiungere prestazioni maggiori.

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Feder, Hans Jacob Sverdrup. "Simultaneous stochastic mapping and localization." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/9411.

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Folkesson, John. "Simultaneous localization and mapping with robots." Doctoral thesis, Stockholm, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-445.

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Maddern, William Paul. "Continuous appearance-based localisation and mapping." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/65841/2/William_Maddern_Thesis.pdf.

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This thesis presents a novel approach to mobile robot navigation using visual information towards the goal of long-term autonomy. A novel concept of a continuous appearance-based trajectory is proposed in order to solve the limitations of previous robot navigation systems, and two new algorithms for mobile robots, CAT-SLAM and CAT-Graph, are presented and evaluated. These algorithms yield performance exceeding state-of-the-art methods on public benchmark datasets and large-scale real-world environments, and will help enable widespread use of mobile robots in everyday applications.

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Tiranti, Luca. "Simultaneous localization and mapping using radar images." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22893/.

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Questa tesi affronta il problema di localizzazione e mappatura simultanea in locali indoor utilizzando la tecnologia radar a onde millimetriche. Gli scenari considerati e le tecnologie impiegate sono in linea con il concetto di “personal mobile radar” rendendo questo lavoro un proof-of-concept di tale idea, testandone le performance in ambienti reali attraverso differenti campagne di misura. In accordo con tale concetto, sarà possibile integrare in dispositivi mobili, quali smartphone e tablet, array di antenne che scansioneranno autonomamente l’ambiente circostante e permetteranno di raggiungere una soluzione con specifiche simili alle più performanti soluzioni di SLAM come la tecnologia laser o lidar. Al contempo, l’utilizzo di tecnologia a onde millimetriche permette un possibile impiego del radar personale anche in ambienti con scarsa visibilità trovando applicazione, ad esempio, in contesti industriali ma anche per la sicurezza delle persone mantenendo i costi contenuti ed evitando l'installazione di infrastrutture ad-hoc. Lo campagne di misura in locali indoor svolte per questa tesi hanno reso possibile la raccolta di dati, i quali successivamente sono stati processati e forniti in input all'algoritmo di SLAM proposto sotto forma di “immagini radar”. A partire da queste, verranno presentate diverse strategie sviluppate per la stima della traiettoria del radar e mapping dell’ambiente e, infine, per ognuna di queste, verranno mostrati i relativi risultati ottenuti. Verranno mostrati i risultati ottenuti da campagne di misure sia a 77 GHz che a 300 GHz. Queste ultime sono state condotte presso il centro di ricerca CEA-Leti (Grenoble, France) nel contesto del progetto europeo PRIMELOC (Personal Radars for Radio Imaging and Infrastructure-less Localization) del quale l’Università di Bologna è coordinatrice. I risultati mostreranno come l’aumento delle frequenze in gioco possa portare benefici in termini di accuratezza dei risultati.

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Kee, Vincent P. "Simultaneous Tracking, Object Registration, and Mapping (STORM)." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119560.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 87-91).
An autonomous system needs to be aware of its surroundings and know where it is in its environment in order to operate robustly in unknown environments. This problem is known as Simultaneous Localization and Mapping (SLAM). SLAM techniques have been successfully implemented on systems operating in the real world. However, most SLAM approaches assume that the environment does not change during operation -- the static world assumption. When this assumption is violated (e.g. an object moves), the SLAM estimate degrades. Consequently, the static world assumption prevents robots from interacting with their environments (e.g. manipulating objects) and restricts them to navigating in static environments. Additionally, most SLAM systems generate maps composed of low-level features that lack information about objects and their locations in the scene. This representation limits the map's utility, preventing it from being used for tasks beyond navigation such as object manipulation and task planning. We present Simultaneous Tracking, Object Registration, and Mapping (STORM), a SLAM system that represents an environment as a collection of dynamic objects. STORM enables a robot to build and maintain maps of dynamic environments, use the map estimates to manipulate objects, and localize itself in the map when revisiting the environment. We demonstrate STORM's capabilities with simulation and real-world experiments and compare its performance against that of a typical SLAM approach.
by Vincent P. Kee.
M. Eng.

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Brink, Wikus. "Stereo vision for simultaneous localization and mapping." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71593.

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Thesis (MScEng)--Stellenbosch University, 2012.
ENGLISH ABSTRACT: Simultaneous localization and mapping (SLAM) is vital for autonomous robot navigation. The robot must build a map of its environment while tracking its own motion through that map. Although many solutions to this intricate problem have been proposed, one of the most prominent issues that still needs to be resolved is to accurately measure and track landmarks over time. In this thesis we investigate the use of stereo vision for this purpose. In order to find landmarks in images we explore the use of two feature detectors: the scale-invariant feature transform (SIFT) and speeded-up robust features (SURF). Both these algorithms find salient points in images and calculate a descriptor for each point that is invariant to scale, rotation and illumination. By using the descriptors we match these image features between stereo images and use the geometry of the system to calculate a set of 3D landmark measurements. A Taylor approximation of this transformation is used to derive a Gaussian noise model for the measurements. The measured landmarks are matched to landmarks in a map to find correspondences. We find that this process often incorrectly matches ambiguous landmarks. To find these mismatches we develop a novel outlier detection scheme based on the random sample consensus (RANSAC) framework. We use a similarity transformation for the RANSAC model and derive a probabilistic consensus measure that takes the uncertainties of landmark locations into account. Through simulation and practical tests we find that this method is a significant improvement on the standard approach of using the fundamental matrix. With accurately identified landmarks we are able to perform SLAM. We investigate the use of three popular SLAM algorithms: EKF SLAM, FastSLAM and FastSLAM 2. EKF SLAM uses a Gaussian distribution to describe the systems states and linearizes the motion and measurement equations with Taylor approximations. The two FastSLAM algorithms are based on the Rao-Blackwellized particle filter that uses particles to describe the robot states, and EKFs to estimate the landmark states. FastSLAM 2 uses a refinement process to decrease the size of the proposal distribution and in doing so decreases the number of particles needed for accurate SLAM. We test the three SLAM algorithms extensively in a simulation environment and find that all three are capable of very accurate results under the right circumstances. EKF SLAM displays extreme sensitivity to landmark mismatches. FastSLAM, on the other hand, is considerably more robust against landmark mismatches but is unable to describe the six-dimensional state vector required for 3D SLAM. FastSLAM 2 offers a good compromise between efficiency and accuracy, and performs well overall. In order to evaluate the complete system we test it with real world data. We find that our outlier detection algorithm is very effective and greatly increases the accuracy of the SLAM systems. We compare results obtained by all three SLAM systems, with both feature detection algorithms, against DGPS ground truth data and achieve accuracies comparable to other state-of-the-art systems. From our results we conclude that stereo vision is viable as a sensor for SLAM.
AFRIKAANSE OPSOMMING: Gelyktydige lokalisering en kartering (simultaneous localization and mapping, SLAM) is ’n noodsaaklike proses in outomatiese robot-navigasie. Die robot moet ’n kaart bou van sy omgewing en tegelykertyd sy eie beweging deur die kaart bepaal. Alhoewel daar baie oplossings vir hierdie ingewikkelde probleem bestaan, moet een belangrike saak nog opgelos word, naamlik om landmerke met verloop van tyd akkuraat op te spoor en te meet. In hierdie tesis ondersoek ons die moontlikheid om stereo-visie vir hierdie doel te gebruik. Ons ondersoek die gebruik van twee beeldkenmerk-onttrekkers: scale-invariant feature transform (SIFT) en speeded-up robust features (SURF). Altwee algoritmes vind toepaslike punte in beelde en bereken ’n beskrywer vir elke punt wat onveranderlik is ten opsigte van skaal, rotasie en beligting. Deur die beskrywer te gebruik, kan ons ooreenstemmende beeldkenmerke soek en die geometrie van die stelsel gebruik om ’n stel driedimensionele landmerkmetings te bereken. Ons gebruik ’n Taylor- benadering van hierdie transformasie om ’n Gaussiese ruis-model vir die metings te herlei. Die gemete landmerke se beskrywers word dan vergelyk met dié van landmerke in ’n kaart om ooreenkomste te vind. Hierdie proses maak egter dikwels foute. Om die foutiewe ooreenkomste op te spoor het ons ’n nuwe uitskieterherkenningsalgoritme ontwikkel wat gebaseer is op die RANSAC-raamwerk. Ons gebruik ’n gelykvormigheidstransformasie vir die RANSAC-model en lei ’n konsensusmate af wat die onsekerhede van die ligging van landmerke in ag neem. Met simulasie en praktiese toetse stel ons vas dat die metode ’n beduidende verbetering op die standaardprosedure, waar die fundamentele matriks gebruik word, is. Met ons akkuraat geïdentifiseerde landmerke kan ons dan SLAM uitvoer. Ons ondersoek die gebruik van drie SLAM-algoritmes: EKF SLAM, FastSLAM en FastSLAM 2. EKF SLAM gebruik ’n Gaussiese verspreiding om die stelseltoestande te beskryf en Taylor-benaderings om die bewegings- en meetvergelykings te lineariseer. Die twee FastSLAM-algoritmes is gebaseer op die Rao-Blackwell partikelfilter wat partikels gebruik om robottoestande te beskryf en EKF’s om die landmerktoestande af te skat. FastSLAM 2 gebruik ’n verfyningsproses om die grootte van die voorstelverspreiding te verminder en dus die aantal partikels wat vir akkurate SLAM benodig word, te verminder. Ons toets die drie SLAM-algoritmes deeglik in ’n simulasie-omgewing en vind dat al drie onder die regte omstandighede akkurate resultate kan behaal. EKF SLAM is egter baie sensitief vir foutiewe landmerkooreenkomste. FastSLAM is meer bestand daarteen, maar kan nie die sesdimensionele verspreiding wat vir 3D SLAM vereis word, beskryf nie. FastSLAM 2 bied ’n goeie kompromie tussen effektiwiteit en akkuraatheid, en presteer oor die algemeen goed. Ons toets die hele stelsel met werklike data om dit te evalueer, en vind dat ons uitskieterherkenningsalgoritme baie effektief is en die akkuraatheid van die SLAM-stelsels beduidend verbeter. Ons vergelyk resultate van die drie SLAM-stelsels met onafhanklike DGPS-data, wat as korrek beskou kan word, en behaal akkuraatheid wat vergelykbaar is met ander toonaangewende stelsels. Ons resultate lei tot die gevolgtrekking dat stereo-visie ’n lewensvatbare sensor vir SLAM is.

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47

Vidiyala, Sai Krishna. "Simultaneous localization and mapping with radio signals." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24138/.

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Simultaneous localization and mapping is a concept that originated in the robotics area, where robots are traditionally equipped with a laser sensor and can navigate and map an unknown surrounding environment. High-definition distance estimates and very narrow steering beams are provided by high-accuracy SLAM technologies based on laser and mechanical steering devices. Unfortunately, these technologies are prohibitively expensive and cumbersome, and they cannot be integrated into mobile devices. This Thesis investigates millimeter wave (mmWave) radar technology to solve a SLAM problem. The technologies used are consistent with the concept of a personal mobile radar and they can be considered a proof-of-concept for further development of this concept.

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48

Lang, Adam, and Andreas Fröderberg. "An introductary view into Simultaneous LocalizationAnd Mapping." Thesis, KTH, Maskinkonstruktion (Inst.), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-226330.

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In recent years autonomous vehicles have gained attention as being the potential future for the automotive industry. A central part of an autonomous vehicle is the way that the vehicle locates itself relative to the surrounding environment. This localization can be efficiently performed simultaneously as building a map of the environment, a process called Simultaneous Localization And Mapping or SLAM. In this thesis, the basic algorithms that lay the foundation for the current state of the art of SLAM, and their refinements are studied with the goal of procuring a comprehensive list. The secondary goal is to create a simulation platform for assessing the performance of different algorithms. The thesis is commissioned by automotive consultancy AVL that will use the results to build their knowledge base within the autonomous field. Each algorithm that is mentioned has been looked at from a historical, mathematical and application perspective. This has resulted in a literature study containing thirteen algorithms and a table containing a categorization for easy and quick comparison. Categories of SLAM algorithms are identified to be map type, complexity, hypothesis and basic algorithm. Among the basic algorithms, the classic Kalman Filter and Particle filters have been found, along with modern Maximum a Posteriori algorithms. Furthermore, EKF and fastSLAM have been simulated in a Matlab environment for comparison. A development and simulation platform has also been developed in software frameworks Gazebo and ROS. This platform ensures modularity where algorithms can be exchanged and simulations can be run simultaneously as a real life implementation. The results from the simulations show that the simulation in Gazebo can produce similar results as the traditional simulations in Matlab. It is difficult to know exactly which algorithms that are used in commercial applications. Using the information from the literature review, a discussion is made regarding which algorithms may be suitable for real life usage. The conclusion is that most likely, combinations of several algorithms are used in order to leverage the benefits from each, as well as mitigate weaknesses.
På senare tid har autonoma fordon fått uppmärksamhet som en möjlig framtid för fordonsindustrin. En central del i ett autonomt fordon är hur det lokaliserar sig i sin omgivning. Lokaliseringen kan göras effektivt samtidigt som fordonet bygger en karta av sin omgivning, en process som kallas Simultaneous Localization and Mapping, eller SLAM. I detta examensarbete undersöks algoritmerna som utgör grunden till den nuvarande framkanten i ämnet, samt hur de kan förfinas. Det primära målet är att göra en lista som grundligt kategoriserar algoritmerna. Det sekundära målet är att skapa en simuleringsplatform där de olika algoritmernas egenskaper kan jämföras. Arbetet är beställt av konsultfirman AVL som verkar inom fordonsindustrin och ska användas för att bygga AVLs kunskapsbas inom autonoma fordon. Varje algoritm som undersökts skärskådas i en historisk, matematisk och tillämpad synvinkel. Detta har resulterat i en litteraturstudie som innehåller tretton olika algoritmer och en tabell som innehåller kategorisering for snabb och enkel jämförelse. Kategorier som identifierats är typ av karta, komplexitet, hypotes och grundläggande algoritm. Bland dessa finns de klassiska Kalman- och Partikelfilterna samt de mer moderna Maximum a Posteriori-algoritmerna. Vidare har EKF och fastSLAM simulerats i en Matlab-miljö för att jämföra prestanda mellan två olika typer av algoritmer. En mjukvaruplattform har utvecklats i Gazebo och ROS för att kunna simulera och jämföra algoritmer. Plattformen baseras på modularitet där algoritmer kan simuleras samtidigt som de körs på riktig hårdvara. Resultaten från experimenten visar att den utvecklade testplattformen presterar likvärdigt som teoretiska simuleringar i Matlab. Det är svårt att veta exakt vilka algoritmer som används i kommersiella implementeringar av SLAM. Genom att använda informationen från litteraturstudien görs en diskussion om vilka algoritmer som är lämpliga för riktiga applikationer. Slutsatsen är att kombinationer av grundalgoritmerna antagligen används för att utnyttja fördelarna med dem, samtidigt som nackdelarnas inflytande minskas.

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49

Sünderhauf, Niko. "Robust optimization for simultaneous localization and mapping." Thesis, Technischen Universitat Chemnitz, 2012. https://eprints.qut.edu.au/109667/1/109667.pdf.

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SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently. Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers . In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far. The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem’s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets. This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.

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50

Sünderhauf, Niko. "Robust Optimization for Simultaneous Localization and Mapping." Doctoral thesis, Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-86443.

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SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently. Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers. In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far. The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem\'s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets. This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.

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