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HASSANZADEH, Aidin. "Mobile Robot Wind Mapping." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-34606.
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Statistical gas distribution mapping has recently become a prominent research area in the robotics community. Gas distribution mapping using mobile robots aims for building map of gas dispersion in an unknown environment using the sampled gas concentrations accompanied by the corresponding atmospheric variables. In this context, wind is considered as one of the main driving forces and recently exploited as an environmental bias in the the modelling process. However, the existing approaches utilizing the wind data are based on very simple averaging window methods which do not take the specic spatio-temporal wind variations into account appropriately. In the current thesis work, under the heading of statistical wind modelling, the various aspects of the existing approaches to model both temporal and spatial wind variations are studied. Accordingly, in the undertaking of Mobile Robot Wind Mapping (MRWM) task, three individual methods for statistically wind speed modelling, wind direction modelling and spatial wind mapping are proposed and implemented. Particularly, wind speed is modelled in form of a Gaussian distribution where the valid averaging scale is dened using an online adaptive approach, namely Time-Dependent Memory Method (TDMM) . The wind direction is modelled by means of the mixturemodel of Von-Mises distribution and for the spatial mapping of modelled wind data, a recursive approach based on Linear Kalman lter is utilized. The proposed approaches for statistically wind speed and direction modelling are applied to and evaluated by real wind data, collected specically for this project. The wind mapping algorithm is implemented and tested using simulated data.
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Wong, Chee Kit. "Cognitive inspired mapping by an autonomous mobile robot." Click here to access this resource online, 2008. http://hdl.handle.net/10292/427.
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When animals explore a new environment, they do not acquire a precise map of the places visited. In fact, research has shown that learning is a recurring process. Over time, new information helps the animal to update their perception of the locations it has visited. Yet, they are still able to use the fuzzy and often incomplete representation to find their way home. This process has been termed the cognitive mapping process. The work presented in this thesis uses a mobile robot equipped with sonar sensors to investigate the nature of such a process. Specifically, what is the information that is fundamental and prevalent in spatial navigation? Initially, the robot is instructed to compute a “cognitive map” of its environment. Since a robot is not a cognitive agent, it cannot, by definition, compute a cognitive map. Hence the robot is used as a test bed for understanding the cognitive mapping process. Yeap’s (1988) theory of cognitive mapping forms the foundation for computing the robot’s representation of the places it has visited. He argued that a network of local spaces is computed early in the cognitive mapping process. Yeap coined these local spaces as Absolute Space Representations (ASRs). However, ASR is not just a process of partitioning the environment into smaller local regions. The ASRs describe the bounded space that one is in, how one could leave that space (exits) and how the exits serves to link the ASRs to form a network that serves as the cognitive map (see Jefferies (1999)). Like the animal’s cognitive map, ASRs are not precise geometrical maps of the environment but rather, provide a rough shape or feel of the space the robot is currently in. Once the robot computes its “cognitive map”, it is then, like foraging and hoarding animals, instructed to find its way home. To do so, the robot uses two crucial pieces of information: distance between exits of ASRs and relative orientation of adjacent ASRs. A simple animal-like strategy was implemented for the robot to locate home. Results from the experiments demonstrated the robot’s ability to determine its location within the visited environment along its journey. This task was performed without the use of an accurate map. From these results and reviews of various findings related to cognitive mapping for various animals, we deduce that: Different animals have different sensing capabilities. They live in different environments and therefore face unique challenges. Consequently, they evolve to have different navigational strategies. However, we believe two crucial pieces of information are inherent in all animals and form the fundamentals of navigation: distance and orientation. Higher level animals may encode and may even prefer richer information to enhance the animal’s cognitive map. Nonetheless, distance and orientation will always be computed as a core process of cognitive mapping. We believe this insight will help future research to better understand the complex nature of cognitive mapping.
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WANG, XUAN. "2D Mapping Solutionsfor Low Cost Mobile Robot." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138427.
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Mapping, localization, and path-planning are three fundamental problems of robotic. Robot needs a map to perform actions like path-planning. When positioning system is not available, the map is also used for localization. A lot of researches have been done in this area. And newly emerging ranging sensors, like Kinect and TOF camera, have widen people’s choices and greatly enhanced innovative ideas in robot mapping. The price of these sensors is not very high and the performance is decent, which makes low cost, high performance mobile robot solution possible. In this thesis, different existing state of the art mapping methods are studied. Based on literature studies, different ranging sensors for mapping are evaluated. And by using the 3D ranging sensor, three mapping methods are implemented. Occupancy grid map with scan matching, feature-grid hybrid map with map pruning and simple points map with ICP algorithm. Basic potential field path-planning is also implemented. The experiment results illustrate the performance of each method.
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Nordin, Peter. "Mobile Robot Traversability Mapping : For Outdoor Navigation." Licentiate thesis, Linköpings universitet, Fluida och mekatroniska system, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-85937.
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To avoid getting stuck or causing damage to a vehicle or its surroundings a driver must be able to identify obstacles and adapt speed to ground conditions. An automatically controlled vehicle must be able to handle these identifications and adjustments by itself using sensors, actuators and control software. By storing properties of the surroundings in a map, a vehicle revisiting an area can benefit from prior information. Rough ground may cause oscillations in the vehicle chassis. These can be measured by on-board motion sensors. For obstacle detection, a representation of the geometry of the surroundings can be created using range sensors. Information on where it is suitable to drive, called traversability, can be generated based on these kinds of sensor measurements. In this work, real semi-autonomous mobile robots have been used to create traverasbility maps in both simulated and real outdoor environments. Seeking out problems through experiments and implementing algorithms in an attempt to solve them has been the core of the work. Finding large obstacles in the vicinity of a vehicle is seldom a problem; accurately identifying small near-ground obstacles is much more difficult, however. The work additionally includes both high-level path planning, where no obstacle details are considered, and more detailed planning for finding an obstacle free path. How prior maps can be matched and merged in preparation for path planning operations is also shown. To prevent collisions with unforeseen objects, up-to-date traversability information is used in local-area navigation and obstacle avoidance.
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Deďo, Michal. "Řízení čtyřkolového mobilního robotu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2011. http://www.nusl.cz/ntk/nusl-229688.
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The purpose of this thesis is to design and implement four-wheel mobile robot control which will be used in future in the field of mapping and localization. Concretely, it will be a design of drive control with microcontrollers Xmega, which will also process the signals of the sensors. Communication with the PC will ensure the BlueTooth module. In view of the future use of the robot, there will be designed and carried out modifications of the mechanical part. Correctness and functionality of all parts of the robot will be verified by carrying out basic movements.
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McCoig, Kenneth. "A MOBILE ROBOTIC COMPUTING PLATFORM FOR THREE-DIMENSIONAL INDOOR MAPPI." Master's thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2372.
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There are several industries exploring solutions to quickly and accurately digitize unexplored indoor environments, into useable three-dimensional databases. Unfortunately, there are inherent challenges to the indoor mapping process such as, scanning limitations and environment complexity, which require a specific application of tools to map an environment precisely with low cost and high speed. This thesis successfully demonstrates the design and implementation of a low cost mobile robotic computing platform with laser scanner, for quickly mapping with high resolution, urban and/or indoor environments using a gyro-enhanced orientation sensor and selectable levels of detail. In addition, a low cost alternative solution to three-dimensional laser scanning is presented, via a standard two-dimensional SICK proximity laser scanner mounted to a custom servo motor mount and controlled by external microcontroller. A software system to control the robot is presented, which incorporates and adheres to widely accepted software engineering guidelines and principles. An analysis of the overall system, including robot specifications, system capabilities, and justification for certain design decisions, are described in detail. Results of various open source software algorithms, as it applies to scan data and image data, are also compared; including evaluation of data correlation and registration techniques. In addition, laser scanner mapping tests, specifications, and capabilities are presented and analyzed. A sample design for converting the final scanned point cloud data to a database is presented and assessed. The results suggest the overall project yields a relatively high degree of accuracy and lower cost over most other existing systems surveyed, as well as, the potential for application of the system in other fields. The results also discuss thoughts for possible future research work.M.S.Cp.E.
Department of Electrical and Computer Engineering
Engineering and Computer Science
Computer Engineering
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Ezequiel, Carlos Favis. "Real-Time Map Manipulation for Mobile Robot Navigation." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4481.
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Mobile robots are gaining increased autonomy due to advances in sensor and computing technology. In their current form however, robots still lack algorithms for rapid perception of objects in a cluttered environment and can benefit from the assistance of a human operator. Further, fully autonomous systems will continue to be computationally expensive and costly for quite some time. Humans can visually assess objects and determine whether a certain path is traversable, but need not be involved in the low-level steering around any detected obstacles as is necessary in remote-controlled systems. If only used for rapid perception tasks, the operator could potentially assist several mobile robots performing various tasks such as exploration, surveillance, industrial work and search and rescue operations. There is a need to develop better human-robot interaction paradigms that would allow the human operator to effectively control and manage one or more mobile robots.
This paper proposes a method of enhancing user effectiveness in controlling multiple mobile robots through real-time map manipulation. An interface is created that would allow a human operator to add virtual obstacles to the map that represents areas that the robot should avoid. A video camera is connected to the robot that would allow a human user to view the robot's environment. The combination of real-time map editing and live video streaming enables the robot to take advantage of human vision, which is still more effective at general object identification than current computer vision technology. Experimental results show that the robot is able to plan a faster path around an obstacle when the user marks the obstacle on the map, as opposed to allowing the robot to navigate on its own around an unmapped obstacle. Tests conducted on multiple users suggest that the accuracy in placing obstacles on the map decreases with increasing distance of the viewing apparatus from the obstacle. Despite this, the user can take advantage of landmarks found in the video and in the map in order to determine an obstacle's position on the map.
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Casalaro, Giuseppina Lucia, and Giulio Cattivera. "MODEL-DRIVEN ENGINEERING FOR MOBILE ROBOT SYSTEMS: A SYSTEMATIC MAPPING STUDY." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-28261.
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The development of autonomous Mobile Robot Systems is attracting nowadays more and moreinterest from both researchers and practitioners, mainly because they may open for a wide rangeof improvements for quality of life. Mobile robots are systems capable of accomplishing missionsby moving in an unknown environment without human supervision. Throughout mechanisms ofdetection, communication and adaptation, they can adapt their behavior according to changes ofthe environment. Individual robots can even join teams of autonomous mobile robots that, throughindividual tasks, accomplish common missions. These are called Mobile Multi-Robot Systems andare meant to perform missions that a single robot would not be able to carry out by itself.When it comes to the development of Mobile Robot Systems, currently there is no standard methodology.This is mainly due to the complexity of the domain and the variety of di↵erent platformsthat are available on the market. A promising methodology that recently has gained attention insoftware industry for its ability of mitigating complexity and boosting platform-independence, isModel-Driven Engineering.This thesis proposes a systematic mapping study on the state-of-the-art of Model-Driven Engineeringfor Mobile Robot Systems. Through our contribution, researchers can get a picture of theactual trends and open challenges for further research, while practitioners can realize the suitabilityof Model-Driven Engineering by checking to what extent it has been applied to real-world projects.
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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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Sezginalp, Emre. "Simultaneous Localization And Mapping For A Mobile Robot Operating In Outdoor Environments." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12609191/index.pdf.
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In this thesis, a method to the solution of autonomous navigation problem of a robot
working in an outdoor application is sought. The robot will operate in unknown
terrain where there is no a priori map present, and the robot must localize itself while
simultaneously mapping the environment. This is known as Simultaneous
Localization and Mapping (SLAM) problem in the literature. The SLAM problem is
attempted to be solved by using the correlation between range data acquired at
different poses of the robot. A robot operating outdoors will traverse unstructured
terrain, therefore for localization, pitch, yaw and roll angles must also be taken into
account along with the (x,y,z) coordinates of the robot. The Iterative Closest Points
(ICP) algorithm is used to find this transformation between different poses of the
robot and find its location. In order to collect the range data, a system composing of a
laser range finder and an angular positioning system is used. During localization and
mapping, odometry data is fused with range data.
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Meger, David Paul. "Planning, localization, and mapping for a mobile robot in a camera network." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=101623.
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Networks of cameras such as building security systems can be a source of localization information for a mobile robot assuming a map of camera locations as well as calibration information for each camera is available. This thesis describes an automated system to acquire such information. A fully automated camera calibration system uses fiducial markers and a mobile robot in order to drastically improve ease-of-use compared to standard techniques. A 6DOF EKF is used for mapping and is validated experimentally over a 50 m hallway environment. Motion planning strategies are considered both in front of a single camera to maximize calibration accuracy and globally between cameras in order to facilitate accurate measurements. For global motion planning, an adaptive exploration strategy based on heuristic search allows compromise between distance traveled and final map uncertainty which provides the system a level of autonomy which could not be obtained with previous techniques.
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Reggente, Matteo. "Statistical gas distribution modelling for mobile robot applications." Doctoral thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-37896.
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In this dissertation, we present and evaluate algorithms for statistical gas distribution modelling in mobile robot applications. We derive a representation of the gas distribution in natural environments using gas measurements collected with mobile robots. The algorithms fuse different sensors readings (gas, wind and location) to create 2D or 3D maps. Throughout this thesis, the Kernel DM+V algorithm plays a central role in modelling the gas distribution. The key idea is the spatial extrapolation of the gas measurement using a Gaussian kernel. The algorithm produces four maps: the weight map shows the density of the measurements; the confidence map shows areas in which the model is considered being trustful; the mean map represents the modelled gas distribution; the variance map represents the spatial structure of the variance of the mean estimate. The Kernel DM+V/W algorithm incorporates wind measurements in the computation of the models by modifying the shape of the Gaussian kernel according to the local wind direction and magnitude. The Kernel 3D-DM+V/W algorithm extends the previous algorithm to the third dimension using a tri-variate Gaussian kernel. Ground-truth evaluation is a critical issue for gas distribution modelling with mobile platforms. We propose two methods to evaluate gas distribution models. Firstly, we create a ground-truth gas distribution using a simulation environment, and we compare the models with this ground-truth gas distribution. Secondly, considering that a good model should explain the measurements and accurately predicts new ones, we evaluate the models according to their ability in inferring unseen gas concentrations. We evaluate the algorithms carrying out experiments in different environments. We start with a simulated environment and we end in urban applications, in which we integrated gas sensors on robots designed for urban hygiene. We found that typically the models that comprise wind information outperform the models that do not include the wind data.
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Matsumoto, Takeshi, and takeshi matsumoto@flinders edu au. "Real-Time Multi-Sensor Localisation and Mapping Algorithms for Mobile Robots." Flinders University. Computer Science, Engineering and Mathematics, 2010. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20100302.131127.
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A mobile robot system provides a grounded platform for a wide variety of interactive systems to be developed and deployed. The mobility provided by the robot presents unique challenges as it must observe the state of the surroundings while observing the state of itself with respect to the environment. The scope of the discipline includes the mechanical and hardware issues, which limit and direct the capabilities of the software considerations. The systems that are integrated into the mobile robot platform include both specific task oriented and fundamental modules that define the core behaviour of the robot. While the earlier can sometimes be developed separately and integrated at a later stage, the core modules are often custom designed early on to suit the individual robot system depending on the configuration of the mechanical components.
This thesis covers the issues encountered and the resolutions that were implemented during the development of a low cost mobile robot platform using off the shelf sensors, with a particular focus on the algorithmic side of the system. The incrementally developed modules target the localisation and mapping aspects by incorporating a number of different sensors to gather the information of the surroundings from different perspectives by simultaneously or sequentially combining the measurements to disambiguate and support each other. Although there is a heavy focus on the image processing techniques, the integration with the other sensors and the characteristics of the platform itself are included in the designs and analyses of the core and interactive modules.
A visual odometry technique is implemented for the localisation module, which includes calibration processes, feature tracking, synchronisation between multiple sensors, as well as short and long term landmark identification to calculate the relative pose of the robot in real time. The mapping module considers the interpretation and the representation of sensor readings to simplify and hasten the interactions between multiple sensors, while selecting the appropriate attributes and characteristics to construct a multi-attributed model of the environment.
The modules that are developed are applied to realistic indoor scenarios, which are taken into consideration in some of the algorithms to enhance the performance through known constraints. As the performance of algorithms depends significantly on the hardware, the environment, and the number of concurrently running sensors and modules, comparisons are made against various implementations that have been developed throughout the project.
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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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Easton, Adam. "From mobile robot localisation to multi-robot exploration : a Gaussian approach to localisation and mapping in large environments." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442398.
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Wijk, Olle. "Triangulation Based Fusion of Sonar Data with Application in Mobile Robot Mapping and Localization." Doctoral thesis, Stockholm : Tekniska högsk, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3124.
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Dogruer, Can Ulas. "Global Urban Localization Of An Outdoor Mobile Robot Using Satellite Images." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610446/index.pdf.
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In this dissertation, the mapping of outdoor environments and localization of a mobile robot in that setting is considered. It is well known that in the absence of a map or precise pose estimates, localization and mapping is a coupled problem. However, in this dissertation this problem is decoupled in to two disjoint stepsmapping and localization on the acquired map. First the images of the outdoor environment is downloaded from a website such as Google Earth and then these images are processed by utilizing several artificial neural network topologies to create maps. Once these maps are obtained, the localization is done by using Monte Carlo localization. This dissertation addresses a solution for the information which is most of the time taken for granted in most studies
a prior map of environment. Mapping is solved by using a novel approach
the map of the environment is created by processing satellite images. Several global localization techniques are developed and evaluated to be used with these map so as to localize a mobile robot globally. The outcome of this novel approach presented here may serve as a virtual GPS. Mobile phone applications can localize a user within a circle of uncertainty without GPS. This crude localization may be used to download relevant satellite images of the local environment. Once the mobile robot is localized on the map created from the satellite images by using available techniques in the literature i.e. Monte Carlo localization, it may be claimed that it is localized on Earth.
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Althaus, Philipp. "Indoor Navigation for Mobile Robots : Control and Representations." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3644.
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This thesis deals with various aspects of indoor navigationfor mobile robots. For a system that moves around in ahousehold or office environment,two major problems must betackled. First, an appropriate control scheme has to bedesigned in order to navigate the platform. Second, the form ofrepresentations of the environment must be chosen.
Behaviour based approaches have become the dominantmethodologies for designing control schemes for robotnavigation. One of them is the dynamical systems approach,which is based on the mathematical theory of nonlineardynamics. It provides a sound theoretical framework for bothbehaviour design and behaviour coordination. In the workpresented in this thesis, the approach has been used for thefirst time to construct a navigation system for realistic tasksin large-scale real-world environments. In particular, thecoordination scheme was exploited in order to combinecontinuous sensory signals and discrete events for decisionmaking processes. In addition, this coordination frameworkassures a continuous control signal at all times and permitsthe robot to deal with unexpected events.
In order to act in the real world, the control system makesuse of representations of the environment. On the one hand,local geometrical representations parameterise the behaviours.On the other hand, context information and a predefined worldmodel enable the coordination scheme to switchbetweensubtasks. These representations constitute symbols, on thebasis of which the system makes decisions. These symbols mustbe anchored in the real world, requiring the capability ofrelating to sensory data. A general framework for theseanchoring processes in hybrid deliberative architectures isproposed. A distinction of anchoring on two different levels ofabstraction reduces the complexity of the problemsignificantly.
A topological map was chosen as a world model. Through theadvanced behaviour coordination system and a proper choice ofrepresentations,the complexity of this map can be kept at aminimum. This allows the development of simple algorithms forautomatic map acquisition. When the robot is guided through theenvironment, it creates such a map of the area online. Theresulting map is precise enough for subsequent use innavigation.
In addition, initial studies on navigation in human-robotinteraction tasks are presented. These kinds of tasks posedifferent constraints on a robotic system than, for example,delivery missions. It is shown that the methods developed inthis thesis can easily be applied to interactive navigation.Results show a personal robot maintaining formations with agroup of persons during social interaction.
Keywords:mobile robots, robot navigation, indoornavigation, behaviour based robotics, hybrid deliberativesystems, dynamical systems approach, topological maps, symbolanchoring, autonomous mapping, human-robot interaction
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HERRERA, LUIS ERNESTO YNOQUIO. "MOBILE ROBOT SIMULTANEOUS LOCALIZATION AND MAPPING USING DP-SLAM WITH A SINGLE LASER RANGE FINDER." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=34617@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
SLAM (Mapeamento e Localização Simultânea) é uma das áreas mais pesquisadas na Robótica móvel. Trata-se do problema, num robô móvel, de construir um mapa sem conhecimento prévio do ambiente e ao mesmo tempo manter a sua localização nele. Embora a tecnologia ofereça sensores cada vez mais precisos, pequenos erros na medição são acumulados comprometendo a precisão na localização, sendo estes evidentes quando o robô retorna a uma posição inicial depois de percorrer um longo caminho. Assim, para melhoria do desempenho do SLAM é necessário representar a sua formulação usando teoria das probabilidades. O SLAM com Filtro Extendido de Kalman (EKF-SLAM) é uma solução básica, e apesar de suas limitações é a técnica mais popular. O Fast SLAM, por outro lado, resolve algumas limitações do EKF-SLAM usando uma instância do filtro de partículas conhecida como Rao-Blackwellized. Outra solução bem sucedida é o DP-SLAM, o qual usa uma representação do mapa em forma de grade de ocupação, com um algoritmo hierárquico que constrói mapas 2D bastante precisos. Todos estes algoritmos usam informação de dois tipos de sensores: odômetros e sensores de distância. O Laser Range Finder (LRF) é um medidor laser de distância por varredura, e pela sua precisão é bastante usado na correção do erro em odômetros. Este trabalho apresenta uma detalhada implementação destas três soluções para o SLAM, focalizado em ambientes fechados e estruturados. Apresenta-se a construção de mapas 2D e 3D em terrenos planos tais como em aplicações típicas de ambientes fechados. A representação dos mapas 2D é feita na forma de grade de ocupação. Por outro lado, a representação dos mapas 3D é feita na forma de nuvem de pontos ao invés de grade, para reduzir o custo computacional. É considerado um robô móvel equipado com apenas um LRF, sem nenhuma informação de odometria. O alinhamento entre varreduras laser é otimizado fazendo o uso de Algoritmos Genéticos. Assim, podem-se construir mapas e ao mesmo tempo localizar o robô sem necessidade de odômetros ou outros sensores. Um simulador em Matlab é implementado para a geração de varreduras virtuais de um LRF em um ambiente 3D (virtual). A metodologia proposta é validada com os dados simulados, assim como com dados experimentais obtidos da literatura, demonstrando a possibilidade de construção de mapas 3D com apenas um sensor LRF.
Simultaneous Localization and Mapping (SLAM) is one of the most widely researched areas of Robotics. It addresses the mobile robot problem of generating a map without prior knowledge of the environment, while keeping track of its position. Although technology offers increasingly accurate position sensors, even small measurement errors can accumulate and compromise the localization accuracy. This becomes evident when programming a robot to return to its original position after traveling a long distance, based only on its sensor readings. Thus, to improve SLAM s performance it is necessary to represent its formulation using probability theory. The Extended Kalman Filter SLAM (EKF-SLAM) is a basic solution and, despite its shortcomings, it is by far the most popular technique. Fast SLAM, on the other hand, solves some limitations of the EKFSLAM using an instance of the Rao-Blackwellized particle filter. Another successful solution is to use the DP-SLAM approach, which uses a grid representation and a hierarchical algorithm to build accurate 2D maps. All SLAM solutions require two types of sensor information: odometry and range measurement. Laser Range Finders (LRF) are popular range measurement sensors and, because of their accuracy, are well suited for odometry error correction. Furthermore, the odometer may even be eliminated from the system if multiple consecutive LRF scans are matched. This works presents a detailed implementation of these three SLAM solutions, focused on structured indoor environments. The implementation is able to map 2D environments, as well as 3D environments with planar terrain, such as in a typical indoor application. The 2D application is able to automatically generate a stochastic grid map. On the other hand, the 3D problem uses a point cloud representation of the map, instead of a 3D grid, to reduce the SLAM computational effort. The considered mobile robot only uses a single LRF, without any odometry information. A Genetic Algorithm is presented to optimize the matching of LRF scans taken at different instants. Such matching is able not only to map the environment but also localize the robot, without the need for odometers or other sensors. A simulation program is implemented in Matlab to generate virtual LRF readings of a mobile robot in a 3D environment. Both simulated readings and experimental data from the literature are independently used to validate the proposed methodology, automatically generating 3D maps using just a single LRF.
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Bore, Nils. "Object Instance Detection and Dynamics Modeling in a Long-Term Mobile Robot Context." Doctoral thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219813.
Full textAbstract:
In the last years, simple service robots such as autonomous vacuum cleaners and lawn mowers have become commercially available and increasingly common. The next generation of service robots should perform more advanced tasks, such as to clean up objects. Robots then need to learn to robustly navigate, and manipulate, cluttered environments, such as an untidy living room. In this thesis, we focus on representations for tasks such as general cleaning and fetching of objects. We discuss requirements for these specific tasks, and argue that solving them would be generally useful, because of their object-centric nature. We rely on two fundamental insights in our approach to understand environments on a fine-grained level. First, many of today's robot map representations are limited to the spatial domain, and ignore that there is a time axis that constrains how much an environment may change during a given period. We argue that it is of critical importance to also consider the temporal domain. By studying the motion of individual objects, we can enable tasks such as general cleaning and object fetching. The second insight comes from that mobile robots are becoming more robust. They can therefore collect large amounts of data from those environments. With more data, unsupervised learning of models becomes feasible, allowing the robot to adapt to changes in the environment, and to scenarios that the designer could not foresee. We view these capabilities as vital for robots to become truly autonomous. The combination of unsupervised learning and dynamics modelling creates an interesting symbiosis: the dynamics vary between different environments and between the objects in one environment, and learning can capture these variations. A major difficulty when modeling environment dynamics is that the whole environment can not be observed at one time, since the robot is moving between different places. We demonstrate how this can be dealt with in a principled manner, by modeling several modes of object movement. We also demonstrate methods for detection and learning of objects and structures in the static parts of the maps. Using the complete system, we can represent and learn many aspects of the full environment. In real-world experiments, we demonstrate that our system can keep track of varied objects in large and highly dynamic environments.Under de senaste åren har enklare service-robotar, såsom autonoma dammsugare och gräsklippare, börjat säljas, och blivit alltmer vanliga. Nästa generations service-robotar förväntas utföra mer komplexa uppgifter, till exempel att städa upp utspridda föremål i ett vardagsrum. För att uppnå detta måste robotarna kunna navigera i ostrukturerade miljöer, och förstå hur de kan bringas i ordning. I denna avhandling undersöker vi abstrakta representationer som kan förverkliga generalla städrobotar, samt robotar som kan hämta föremål. Vi diskuterar vad dessa specifika tillämpningar kräver i form av representationer, och argumenterar för att en lösning på dessa problem vore mer generellt applicerbar på grund av uppgifternas föremåls-centrerade natur. Vi närmar oss uppgiften genom två viktiga insikter. Till att börja medär många av dagens robot-representationer begränsade till rumsdomänen. De utelämnar alltså att modellera den variation som sker över tiden, och utnyttjar därför inte att rörelsen som kan ske under en given tidsperiod är begränsad. Vi argumenterar för att det är kritiskt att också inkorperara miljöns rörelse i robotens modell. Genom att modellera omgivningen på en föremåls-nivå möjliggörs tillämpningar som städning och hämtning av rörliga objekt. Den andra insikten kommer från att mobila robotar nu börjar bli så robusta att de kan patrullera i en och samma omgivning under flera månader. Dekan därför samla in stora mängder data från enskilda omgivningar. Med dessa stora datamängder börjar det bli möjligt att tillämpa så kallade "unsupervised learning"-metoder för att lära sig modeller av enskilda miljöer utan mänsklig inblandning. Detta tillåter robotarna att anpassa sig till förändringar i omgivningen, samt att lära sig koncept som kan vara svåra att förutse på förhand. Vi ser detta som en grundläggande förmåga hos en helt autonom robot. Kombinationen av unsupervised learning och modellering av omgivningens dynamik är intressant. Eftersom dynamiken varierar mellan olika omgivningar,och mellan olika objekt, kan learning hjälpa oss att fånga dessa variationer,och skapa mer precisa dynamik-modeller. Något som försvårar modelleringen av omgivningens dynamik är att roboten inte kan observera hela omgivningen på samma gång. Detta betyder att saker kan flyttas långa sträckor mellan två observationer. Vi visar hur man kan adressera detta i modellen genom att inlemma flera olika sätt som ett föremål kan flyttas på. Det resulterande systemet är helt probabilistiskt, och kan hålla reda på samtliga föremål i robotens omgivning. Vi demonstrerar även metoder för att upptäcka och lära sig föremål i den statiska delen av omgivningen. Med det kombinerade systemet kan vi således representera och lära oss många aspekter av robotens omgivning. Genom experiment i mänskliga miljöer visar vi att systemet kan hålla reda på olika sorters föremål i stora, och dynamiska, miljöer.
QC 20171213
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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.
Full textAbstract:
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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Malartre, Florent. "Perception intelligente pour la navigation rapide de robots mobiles en environnement naturel." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2011. http://tel.archives-ouvertes.fr/tel-00673435.
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Cette thèse concerne la perception de l'environnement pour le guidage automatique d'un robot mobile. Lorsque l'on souhaite réaliser un système de navigation autonome, plusieurs éléments doivent être abordés. Parmi ceux-ci nous traiterons de la franchissabilité de l'environnement sur la trajectoire du véhicule. Cette franchissabilité dépend notamment de la géométrie et du type de sol mais également de la position du robot par rapport à son environnement (dans un repère local) ainsi que l'objectif qu'il doit atteindre (dans un repère global). Les travaux de cette thèse traitent donc de la perception de l'environnement d'un robot au sens large du terme en adressant la cartographie de l'environnement et la localisation du véhicule. Pour cela un système de fusion de données est proposé afin d'estimer ces informations. Ce système de fusion est alimenté par plusieurs capteurs dont une caméra, un télémètre laser et un GPS. L'originalité de ces travaux porte sur la façon de combiner ces informations capteurs. A la base du processus de fusion, nous utilisons un algorithme d'odométrie visuelle basé sur les images de la caméra. Pour accroitre la précision et la robustesse l'initialisation de la position des points sélectionnés se fait grâce à un télémètre laser qui fournit les informations de profondeur. De plus, le positionnement dans un repère global est effectué en combinant cette odométrie visuelle avec les informations GPS. Pour cela un procédé a été mis en place pour assurer l'intégrité de localisation du véhicule avant de fusionner sa position avec les données GPS. La cartographie de l'environnement est toute aussi importante puisqu'elle va permettre de calculer le chemin qui assurera au véhicule une évolution sans risque de collision ou de renversement. Dans cette optique, le télémètre laser déjà présent dans le processus de localisation est utilisé pour compléter la liste courante de points 3D qui matérialisent le terrain à l'avant du véhicule. En combinant la localisation précise du véhicule avec les informations denses du télémètre il est possible d'obtenir une cartographie précise, dense et géo-localisée de l'environnement. Tout ces travaux ont été expérimentés sur un simulateur robotique développé pour l'occasion puis sur un véhicule tout-terrain réel évoluant dans un monde naturel. Les résultats de cette approche ont montré la pertinence de ces travaux pour le guidage autonome de robots mobiles.
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Sola, Ortega Joan. "Towards visual localization, mapping and moving objects tracking by a mobile robot: a geometric and probabilistic approach." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2007. http://tel.archives-ouvertes.fr/tel-00136307.
Full textAbstract:
Dans cette thèse, nous résolvons le problème de reconstruire simultanément une représentation de la géométrie du monde, de la trajectoire de l'observateur, et de la trajectoire des objets mobiles, à l'aide de la vision. Nous divisons le problème en trois étapes : D'abord, nous donnons une solution au problème de la cartographie et localisation simultanées pour la vision monoculaire qui fonctionne dans les situations les moins bien conditionnées géométriquement. Ensuite, nous incorporons l'observabilité 3D instantanée en dupliquant le matériel de vision avec traitement monoculaire. Ceci élimine les inconvénients inhérents aux systèmes stéréo classiques. Nous ajoutons enfin la détection et suivi des objets mobiles proches en nous servant de cette observabilité 3D. Nous choisissons une représentation éparse et ponctuelle du monde et ses objets. La charge calculatoire des algorithmes de perception est allégée en focalisant activement l'attention aux régions de l'image avec plus d'intérêt.
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Solà, Ortega Joan. "Towards visual localization, mapping and moving objects tracking by a mobile robot : a geometric and probabilistic approach." Toulouse, INPT, 2007. http://ethesis.inp-toulouse.fr/archive/00000528/.
Full textAbstract:
Dans cette thèse, nous résolvons le problème de construire simultanément une représentation de la géométrie du monde, de la trajectoire de l'observateur, et de la trajectoire des objets mobiles à l'aide de la vision. Nous divisons le problème en trois étapes. D'abord nous donnons une solution au problème de la cartographie et localisation simultanées pour la vision monoculaire qui fonctionne dans les situations les moins bien conditionnées géométriquement. Ensuite, nous incorporons l'observabilité 3D instantanée en dupliquant le matériel de vision avec traitement monoculaire. Ceci élimine des inconvénients inhérents aux systèmes stéréo classiques. Nous ajoutons enfin la détection et suivi des objets mobiles proches en nous servant de cette observabilité 3D. Nous choisissons une représentation éparse et ponctuelle du monde et ses objets. La charge calculatoire des algorithmes de perception est allégée en focalisant activement l'attention aux régions de l'image avec plus d'intérêtIn this thesis we solve the problem of simultaneously reconstructing a representation of the world geometry, the observer trajectory, and the moving objects trajectories, with the aid of vision. We proceed by dividing the problem into three steps. First, we give a solution to the Simultaneous Localization And Mapping problem (SLAM) for monocular vision that is able to adequately perform in the most ill-conditioned situations : those where the observer approaches the scene in straight line. Second, we incorporate instantaneous 3D observability by duplicating vision hardware with monocular algorithms. This eliminates inherent drawbacks of classic stereo systems. Third, we add detection and tracking of moving objects by making use of this full 3D observability. We choose a sparse, punctual representation of both the world and the moving objects. The computational payload of the perception algorithms is alleviated focusing the attention to those image regions with the highest interest
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Solà, Ortega Joan Devy Michel Monin André. "Towards visual localization, mapping and moving objects tracking by a mobile robot a geometric and probabilistic approach /." Toulouse : INP Toulouse, 2007. http://ethesis.inp-toulouse.fr/archive/00000528.
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El, Hamzaoui Oussama. "Localisation et cartographie simultanées pour un robot mobile équipé d'un laser à balayage : CoreSLAM." Phd thesis, Ecole Nationale Supérieure des Mines de Paris, 2012. http://pastel.archives-ouvertes.fr/pastel-00935600.
Full textAbstract:
La thématique de la navigation autonome constitue l'un des principaux axes de recherche dans le domaine des véhicules intelligents et des robots mobiles. Dans ce contexte, on cherche à doter le robot d'algorithmes et de méthodes lui permettant d'évoluer dans un environnement complexe et dynamique, en toute sécurité et en parfaite autonomie. Dans ce contexte, les algorithmes de localisation et de cartographie occupent une place importante. En effet, sans informations suffisantes sur la position du robot (localisation) et sur la nature de son environnement (cartographie), les autres algorithmes (génération de trajectoire, évitement d'obstacles ...) ne peuvent pas fonctionner correctement. Nous avons centré notre travail de thèse sur une problématique précise : développer un algorithme de SLAM simple, rapide, léger et limitant les erreurs de localisation et de cartographie au maximum sans fermeture de boucle. Au cœur de notre approche, on trouve un algorithme d'IML : Incremental Maximum Likelihood. Ce type d'algorithmes se base sur une estimation itérative de la localisation et de la cartographie. Il est ainsi naturellement divergent. Le choix de l'IML est justifié essentiellement par sa simplicité et sa légèreté. La particularité des travaux réalisés durant cette thèse réside dans les différents outils et algorithmes utilisés afin de limiter la divergence de l'IML au maximum, tout en conservant ses avantages.
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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.
Full textAbstract:
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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Persson, Martin. "Semantic Mapping using Virtual Sensors and Fusion of Aerial Images with Sensor Data from a Ground Vehicle." Doctoral thesis, Örebro : Örebro University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-2186.
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Burian, František. "Tvorba multispektrálních map v mobilní robotice." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-233689.
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The dissertation deals with utilisation of multispectral optical measurement for data fusion that may be used for visual telepresence and indoor/outdoor mapping by heterogeneous mobile robotic system. Optical proximity sensors, thermal imagers, and tricolour cameras are used for the fusion. The described algorithms are optimised to work in real-time and implemented on CASSANDRA robotic system made by our robotic research group.
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Ozgur, Ayhan. "A Novel Mobile Robot Navigation Method Based On Combined Feature Based Scan Matching And Fastslam Algorithm." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612431/index.pdf.
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The main focus of the study is the implementation of a practical indoor localization and
mapping algorithm for large scale, structured indoor environments. Building an incremental
consistent map while also using it for localization is partially unsolved problem and of prime
importance for mobile robot navigation. Within this framework, a combined method
consisting of feature based scan matching and FastSLAM algorithm using LADAR and
odometer sensor is presented. In this method, an improved data association and localization
accuracy is achieved by feeding the SLAM module with better incremental pose information
from scan matching instead of raw odometer output.
This thesis presents the following contributions for indoor localization and mapping. Firstly
a method combining feature based scan matching and FastSLAM is achieved. Secondly,
improved geometrical relations are used for scan matching and also a novel method based on
vector transformation is used for the calculation of pose difference. These are carefully
studied and tuned based on localization and mapping performance failures encountered in
different realistic LADAR datasets. Thirdly, in addition to position, orientation information
usage in line segment and corner oriented data association is presented as an extension in
FastSLAM module.
v
The method is tested with LADAR and odometer data taken from real robot platforms
operated in different indoor environments. In addition to using datasets from the literature,
own datasets are collected on Pioneer 3AT experimental robot platform. As a result, a real
time working localization algorithm which is pretty successive in large scale, structured
environments is achieved.
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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.
Full textAbstract:
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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Machado, Karla Fedrizzi. "Módulo de auto-localização para um agente exploratório usando Filtro de Kalman." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2003. http://hdl.handle.net/10183/26953.
Full textAbstract:
Construir um robô capaz de realizar tarefas sem qualquer interferência humana é um dos maiores desafios da Robótica Move!. Dispondo apenas de sensores, um robô autônomo precisa explorar ambientes desconhecidos e, simultaneamente, construir um mapa confiável a fim de se localizar e realizar a tarefa. Na presença de erros de odometria, o robô não consegue se auto-localizar corretamente em seu mapa interno e acaba por construir um mapa deformado e não condizente com a realidade. Este trabalho apresenta uma solução para o problema da auto-localização de robô moveis autônomos. Esta solução faz use de um método linear de calculo de estimativas chamado Filtro de Kalman para corrigir a posição do robô em seu mapa intern° do ambiente enquanto realiza a exploração. A proposta leva em consideração que toda entidade que se movimenta em um ambiente conta sempre com alguns pontos de referencia para se localizar. Estes pontos são implementados como objetos especiais chamados marcas de Kalman. Em simulação, o reconhecimento das marcas pode ser feito de duas maneiras: através de sua posição no mapa ou através de sua identidade. Nos experimentos realizados em simulação, o método é testado para diferentes erros no angulo de orientação do robô. Os resultados são comparados levando em consideração as deformações no mapa gerado, com e sem marcas de Kalman, e o erro médio da posição do robô durante todo o processo exploratório.Build a robot capable of performing tasks without any human interference is one of the biggest challenges of the Mobile Robotics. Having only sensors, an autonomous robot needs to explore unknown environments and, simultaneously, build a reliable map in order to get its own location and perform the task. In the presence of odometry errors, the robot is not capable of establish its own position on its internal map and ends up building a deformed map that does not reflect reality. This paper presents a solution for the problem of self-localization of autonomous mobile robots. This solution uses a linear method for calculating estimatives called Kalman Filter to correct the robot's position on its internal mapping of the environment while exploring. The proposal considers that any being that moves in an environment always counts on having some reference points to establish its own position. This points are implemented as special objects called Kalman landmarks. In simulation, the recognition of such landmarks can be done in two different ways: through its position on the map or through its identity. In the experiments performed in simulations, the method is tested for different errors in the robot's inclination angle. The results are compared considering the deformations on the generated map, with and without the Kalman landmarks, and the average error of the robot's position during the exploratory process.
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Rogers, John Gilbert. "Life-long mapping of objects and places in domestic environments." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47736.
Full textAbstract:
In the future, robots will expand from industrial and research applications to the home. Domestic service robots will work in the home to perform useful tasks such as object retrieval, cleaning, organization, and security. The tireless support of these systems will not only enable able bodied people to avoid mundane chores; they will also enable the elderly to remain independent from institutional care by providing service, safety, and companionship. Robots will need to understand the relationship between objects and their environments to perform some of these tasks. Structured indoor environments are organized according to architectural guidelines and convenience for their residents. Utilizing this information makes it possible to predict the location of objects. Conversely, one can also predict the function of a room from the detection of a few objects within a given space.
This thesis introduces a framework for combining object permanence and context called the probabilistic cognitive model. This framework combines reasoning about spatial extent of places and the identity of objects and their relationships to one another and to the locations where they appear. This type of reasoning takes into account the context in which objects appear to determine their identity and purpose. The probabilistic cognitive model combines a mapping system called OmniMapper with a conditional random field probabilistic model for context representation. The conditional random field models the dependencies between location and identity in a real-world domestic environment. This model is used by mobile robot systems to predict the effects of their actions during autonomous object search tasks in unknown environments.
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Hähnel, Dirk. "Mapping with mobile robots." [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=974035599.
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Diaz, Espinosa Carlos Andrés. "Uma aplicação de navegação robótica autônoma através de visão computacional estéreo." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/263062.
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Orientador: Paulo Roberto Gardel KurkaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica
Made available in DSpace on 2018-08-16T16:41:02Z (GMT). No. of bitstreams: 1 DiazEspinosa_CarlosAndres_M.pdf: 5130242 bytes, checksum: 334f37aa82bbde2c9ddbfe192baa7c48 (MD5) Previous issue date: 2010
Resumo: O presente trabalho descreve uma técnica de navegação autônoma, utilizando imagens estereoscópicas de câmeras para estimar o movimento de um robô em um ambiente desconhecido. Um método de correlação de pontos em imagens unidimensionais é desenvolvido para a identificação de pontos homólogos de duas imagens em uma cena. Utilizam-se métodos de segmentação de bordas ou contornos para extrair as principais características inerentes nas imagens. Constrói-se um mapa de profundidade dos pontos da imagem com maior similitude dentre os objetos visíveis no ambiente, utilizando um processo de triangulação. Finalmente a estimação do movimento bidimensional do robô é calculada aproveitando a relação epipolar entre dois ou mais pontos em pares de imagens. Experimentos realizados em ambientes virtuais e testes práticos verificam a viabilidade e robustez dos métodos em aplicações de navegação robótica
Abstract: The present work describes a technique for autonomous navigation using stereoscopic camera images to estimate the movement of a robot in an unknown environment. A onedimensional image point correlation method is developed for the identification of similar image points of a scene. Boundary or contour segments are used to extract the principal characteristics of the images. A depth map is built for the points with grater similarity, among the scene objects depicted, using a triangulation process. Finally, the bi-dimensional movement of a robot is estimated through epipolar relations between two or more correlated points in pairs of images. Virtual ambient and practical robot tests are preformed to evaluate the viability of employment and robustness of the proposed techniques
Mestrado
Mecanica dos Sólidos e Projeto Mecanico
Mestre em Engenharia Mecânica
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Pronobis, Andrzej. "Semantic Mapping with Mobile Robots." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-34171.
Full textAbstract:
After decades of unrealistic predictions and expectations, robots have finally escaped from industrial workplaces and made their way into our homes,offices, museums and other public spaces. These service robots are increasingly present in our environments and many believe that it is in the area ofservice and domestic robotics that we will see the largest growth within thenext few years. In order to realize the dream of robot assistants performing human-like tasks together with humans in a seamless fashion, we need toprovide them with the fundamental capability of understanding complex, dynamic and unstructured environments. More importantly, we need to enablethem the sharing of our understanding of space to permit natural cooper-ation. To this end, this thesis addresses the problem of building internalrepresentations of space for artificial mobile agents populated with humanspatial semantics as well as means for inferring that semantics from sensoryinformation. More specifically, an extensible approach to place classificationis introduced and used for mobile robot localization as well as categorizationand extraction of spatial semantic concepts from general place appearance andgeometry. The models can be incrementally adapted to the dynamic changesin the environment and employ efficient ways for cue integration, sensor fu-sion and confidence estimation. In addition, a system and representationalapproach to semantic mapping is presented. The system incorporates and in-tegrates semantic knowledge from multiple sources such as the geometry andgeneral appearance of places, presence of objects, topology of the environmentas well as human input. A conceptual map is designed and used for modelingand reasoning about spatial concepts and their relations to spatial entitiesand their semantic properties. Finally, the semantic mapping algorithm isbuilt into an integrated robotic system and shown to substantially enhancethe performance of the robot on the complex task of active object search. Thepresented evaluations show the effectiveness of the system and its underlyingcomponents and demonstrate applicability to real-world problems in realistichuman settings.QC 20110527
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Huang, Henry. "Bearing-only SLAM : a vision-based navigation system for autonomous robots." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/28599/1/Henry_Huang_Thesis.pdf.
Full textAbstract:
To navigate successfully in a previously unexplored environment, a mobile robot must be able to estimate the spatial relationships of the objects of interest accurately. A Simultaneous Localization and Mapping (SLAM) sys- tem employs its sensors to build incrementally a map of its surroundings and to localize itself in the map simultaneously. The aim of this research project is to develop a SLAM system suitable for self propelled household lawnmowers. The proposed bearing-only SLAM system requires only an omnidirec- tional camera and some inexpensive landmarks. The main advantage of an omnidirectional camera is the panoramic view of all the landmarks in the scene. Placing landmarks in a lawn field to define the working domain is much easier and more flexible than installing the perimeter wire required by existing autonomous lawnmowers. The common approach of existing bearing-only SLAM methods relies on a motion model for predicting the robot’s pose and a sensor model for updating the pose. In the motion model, the error on the estimates of object positions is cumulated due mainly to the wheel slippage. Quantifying accu- rately the uncertainty of object positions is a fundamental requirement. In bearing-only SLAM, the Probability Density Function (PDF) of landmark position should be uniform along the observed bearing. Existing methods that approximate the PDF with a Gaussian estimation do not satisfy this uniformity requirement. This thesis introduces both geometric and proba- bilistic methods to address the above problems. The main novel contribu- tions of this thesis are: 1. A bearing-only SLAM method not requiring odometry. The proposed method relies solely on the sensor model (landmark bearings only) without relying on the motion model (odometry). The uncertainty of the estimated landmark positions depends on the vision error only, instead of the combination of both odometry and vision errors. 2. The transformation of the spatial uncertainty of objects. This thesis introduces a novel method for translating the spatial un- certainty of objects estimated from a moving frame attached to the robot into the global frame attached to the static landmarks in the environment. 3. The characterization of an improved PDF for representing landmark position in bearing-only SLAM. The proposed PDF is expressed in polar coordinates, and the marginal probability on range is constrained to be uniform. Compared to the PDF estimated from a mixture of Gaussians, the PDF developed here has far fewer parameters and can be easily adopted in a probabilistic framework, such as a particle filtering system. The main advantages of our proposed bearing-only SLAM system are its lower production cost and flexibility of use. The proposed system can be adopted in other domestic robots as well, such as vacuum cleaners or robotic toys when terrain is essentially 2D.
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Huang, Henry. "Bearing-only SLAM : a vision-based navigation system for autonomous robots." Queensland University of Technology, 2008. http://eprints.qut.edu.au/28599/.
Full textAbstract:
To navigate successfully in a previously unexplored environment, a mobile robot must be able to estimate the spatial relationships of the objects of interest accurately. A Simultaneous Localization and Mapping (SLAM) sys- tem employs its sensors to build incrementally a map of its surroundings and to localize itself in the map simultaneously. The aim of this research project is to develop a SLAM system suitable for self propelled household lawnmowers. The proposed bearing-only SLAM system requires only an omnidirec- tional camera and some inexpensive landmarks. The main advantage of an omnidirectional camera is the panoramic view of all the landmarks in the scene. Placing landmarks in a lawn field to define the working domain is much easier and more flexible than installing the perimeter wire required by existing autonomous lawnmowers. The common approach of existing bearing-only SLAM methods relies on a motion model for predicting the robot’s pose and a sensor model for updating the pose. In the motion model, the error on the estimates of object positions is cumulated due mainly to the wheel slippage. Quantifying accu- rately the uncertainty of object positions is a fundamental requirement. In bearing-only SLAM, the Probability Density Function (PDF) of landmark position should be uniform along the observed bearing. Existing methods that approximate the PDF with a Gaussian estimation do not satisfy this uniformity requirement. This thesis introduces both geometric and proba- bilistic methods to address the above problems. The main novel contribu- tions of this thesis are: 1. A bearing-only SLAM method not requiring odometry. The proposed method relies solely on the sensor model (landmark bearings only) without relying on the motion model (odometry). The uncertainty of the estimated landmark positions depends on the vision error only, instead of the combination of both odometry and vision errors. 2. The transformation of the spatial uncertainty of objects. This thesis introduces a novel method for translating the spatial un- certainty of objects estimated from a moving frame attached to the robot into the global frame attached to the static landmarks in the environment. 3. The characterization of an improved PDF for representing landmark position in bearing-only SLAM. The proposed PDF is expressed in polar coordinates, and the marginal probability on range is constrained to be uniform. Compared to the PDF estimated from a mixture of Gaussians, the PDF developed here has far fewer parameters and can be easily adopted in a probabilistic framework, such as a particle filtering system. The main advantages of our proposed bearing-only SLAM system are its lower production cost and flexibility of use. The proposed system can be adopted in other domestic robots as well, such as vacuum cleaners or robotic toys when terrain is essentially 2D.
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Kaess, Michael. "Incremental smoothing and mapping." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26572.
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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2009.Committee Chair: Dellaert, Frank; Committee Member: Bobick, Aaron; Committee Member: Christensen, Henrik; Committee Member: Leonard, John; Committee Member: Rehg, James. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Li, Hongjun. "Mapping in uncertain environments for mobile robots." Doctoral thesis, Universidade de Évora, 2019. http://hdl.handle.net/10174/26154.
Full textAbstract:
Um dos problemas fundamentais em robótica móvel é o problema da localização e mapeamento, no qual
um robô se deve localizar ao mesmo tempo que constrói um mapa do ambiente. Existem diversas técnicas
para abordar este problema. Neste trabalho propõem-se abordagens novas para a construção do mapa em
ambientes estáticos e dinâmicos, assumindo pose conhecida.
As abordagens propostas baseiam-se em campos aleatórios de Markov (Markov random fields - MRF) e em
campos aleatórios Gaussianos (Gaussian random fields - GRF), seguindo um ponto de vista Bayesiano, onde
as distribuições de probabilidade a priori são usadas como regularizadores. Num ambiente estático, cada
ponto do espaço é descrito pela sua probabilidade de ocupação. O primeiro método proposto é um filtro
baseado nos MRF, que se centra no ruído das medidas e que pode ser implementado em linha (tempo real).
O segundo método é um método preditivo baseado nos MRF que permite também estimar a probabilidade
de ocupação do espaço não observado. Em ambos os métodos, os mapas são construídos numa grelha de
células. Outra abordagem baseia-se num espaço contínuo, baseado em GRF onde se propõe um método
recursivo de modo a reduzir a complexidade computacional.
No caso de ambientes dinâmicos, a probabilidade de ocupação é substituída pelas probabilidade de transição
duma cadeia de Markov para descrever o comportamento dinâmico de cada ponto. Nesta abordagem são
propostos dois métodos para os ambientes dinâmicos, igualmente baseados nos MRF e nos GRF. No método
com MRF todos os parâmetros são estimados em conjunto. Pelo contrário, com os GRF os parâmetros são
divididos em dois sub-conjuntos de modo a reduzir a complexidade computacional.
Todos os métodos propostos são testados e apresentam-se resultados em simulação nos respetivos capítulos.
Finalmente estes algoritmos são também validados em ambiente experimental. Nestas experiências, as
poses não podem ser medidas com precisão e é tida em consideração a incerteza na pose do robô.
Quando comparados com o estado da arte, os métodos propostos resolvem as inconsistências nos mapas
tendo em consideração a dependência entre pontos vizinhos. Este processo é realizado usando MRF e
GRF em vez de assumir independência. As simulações e os resultados experimentais demonstram que os
métodos propostos podem, não apenas lidar com as inconsistências nos mapas construídos, mas também
tirar proveito da correlação espacial para prever o espaço não observado; Abstract:
Mapping in Uncertain Environments for Mobile
Robots
One of the fundamental problems in robotics is the localization and mapping problem, where a robot has to
localize itself while building a map of the environment. Several techniques exist to tackle this problem. This
work proposes novel mapping approaches with known robot poses for static and dynamic environments.
The proposed techniques are based on Markov random fields (MRFs) and Gaussian random fields (GRFs),
following a Bayesian viewpoint where prior distributions are provided as regularizers. In static environments,
every point is described by its occupancy probability. The first proposed method is an MRF-based filter,
which focuses on the measurement noise and can be implemented online (realtime). The second one
is an MRF-based prediction method, which can also be used to estimate the occupancy probability for
unobserved space. In both methods, the maps are organized as a grid. Another approach, which works in
continuous space, is based on a GRF prediction method, and a recursive algorithm is proposed to reduce
the computational complexity.
In the case of dynamic environments, the occupancy probability is replaced by transition probabilities of a
Markov chain that describe the dynamic behaviour of each point. Two methods for dynamic environments
are proposed, also based on MRFs and GRFs. In the MRF-based method, all the parameters are jointly
estimated. In contrast, in the GRF-based method, the parameters are divided into two subsets to reduce
the computational complexity.
All the proposed methods are tested in simulations in the corresponding chapters. Finally, these algorithms
are also validated on an experimental platform. In the experimental environments, robot poses cannot be
measured precisely, and so the uncertainty of robot poses is also considered.
When compared with the state of the art for dynamic environments, the proposed methods tackle the
inconsistencies in the maps by considering dependence between neighbour points. This is done using MRFs
and GRFs instead of assuming independence. The simulations and the experimental results demonstrate
that the proposed methods can, not only deal with the inconsistency in the built maps, but also take
advantage of the spatial correlation to predict unobserved space.
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Ryde, Julian. "Cooperative 3D Mapping and Localisation of Multiple Mobile Robots." Thesis, University of Essex, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486562.
Full textAbstract:
Developing a coherent and unified approach to localisation and mapping is one of the prerequisites for fully establishing the mobile robotics era in the 21st century. Therefore, this research attempts to address this simultaneous localisation and mapping problem (SLAM) for both a single robot and cooperating multiple robots equipped.with 3D range sensors. To begin with various methods and hardware set-ups for allowing robots to reliably detect each other are explored and tested. Once robots have detected others the question of how their relative pose can be determined most accurately is investigated, especially in the case ·when two corresponding robots detect each other simultaneously. This situation, referred to as mutual localisation, results in reliable and accurate relative pose determination. The second aspect of this work develops hardware mechanisms for mobile robots to obtain 3D information about the environment around them. For robots to rapidly acquire 3D information of their surrounding spatial structure a conventional 2D laser range scanner is augmented with a rotating mirror to build a 3D laser range scanner. A data structure, termed an occupancy list, is devised for efficient probabilistic storing of the 3D map. Algorithms for coherently combining multiple 3D scans from different view points are developed, thus allowing mobile robots to generate an internal representation of their environs.
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Гузик, Петро Євгенович, and Petro Huzyk. "Розробка та дослідження системи для побудови карти з одночасним контролем наявного місцерозташування і пройденого шляху в мобільних автономних засобах." Master's thesis, Тернопіль, ТНТУ, 2020. http://elartu.tntu.edu.ua/handle/lib/33425.
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Роботу виконано на кафедрі ком’пютерно-інтегрованих технологій Тернопільського національного технічного університету імені Івана Пулюя Міністерства освіти і науки України Захист відбудеться 21 грудня 2020 р. о 08 .00 годині на засіданні екзаменаційної комісії № 24 у Тернопільському національному технічному університеті імені Івана Пулюя за адресою: 46001, м. Тернопіль, вул.Руська, 56, навчальний корпус №1, ауд. 403У роботі було розроблено автоматизовану систему для побудови карти з одночасним контролем наявного місцерозташування і пройденого шляху в мобільних автономних засобах. Було розроблено структурну та функціональну схему системи автоматизації. Розроблено функціонал дистанційного керування в ручному режимі використовуючи програмне забезпечення на ПК. Мобільний засіб оснащено камерою і платою-комп’ютером Raspberry Pi 3, драйвером двигуна постійного струму L293d та чотирма двигунами постійного струму. Відеопотік з відеокамери поступає на міні-комп’ютер Raspberry Pi 3, де кожен кадр обробляється, співставляється з попередніми кадрами і будується карта місцевості за допомогою виявлених ознак в кожному кадрі відеопотоку. In the work the automated system for construction of a map with simultaneous control of the available location and the passed way in mobile autonomous means was developed. The structural and functional scheme of the automation system was developed. Developed the functionality of remote control in manual mode using software on a PC. The mobile device is equipped with a camera and a Raspberry Pi 3 computer board, an L293d DC motor driver and four DC motors. The video stream from the camcorder is fed to the Raspberry Pi 3 mini-computer, where each frame is processed, mapped to previous frames, and a terrain map is built using the features detected in each frame of the video stream.
ЗМІСТ ВСТУП 3 1. АНАЛІТИЧНА ЧАСТИНА 4 1.1. Походження проблеми SLAM 4 1.2. Аналіз алгоритмів монокулярного SLAM 8 1.3. Сучасні та альтернативні підходи до вирішення проблеми SLAM 11 1.4. Аналіз реалізацій SLAM алгоритмів 13 2. НАУКОВО-ДОСЛІДНА ЧАСТИНА 16 2.1. Рекурсивне Баєсове оцінювання 16 2.1.1. Рекурсивна оцінка 16 2.1.2. Баєсова оцінка 17 2.2. Представлення просторової карти і стану системи 19 2.3. Баєсова фільтрація 22 2.4. Фільтр Калмана 24 2.5. Розширений фільтр Калмана 26 2.6. Корпускулярний фільтр 27 3. ТЕХНОЛОГІЧНА ЧАСТИНА 30 3.1. Опис конструкції прототипу 30 3.2. Програмна реалізація SLAM алгоритму 33 4. КОНСТРУКТОРСЬКА ЧАСТИНА 37 4.1. Структурні елементи SLAM та симуляція 37 4.1.1. Симуляція карти 39 4.1.2. Симуляція давачів одометрії 42 4.1.3. Симуляція енкодерів 45 4.1.4. Симуляція LiDAR 50 5. СПЕЦІАЛЬНА ЧАСТИНА 55 5.1. Вибір двигунів і енкодерів 55 5.2. Вибір LiDAR 57 5.3. Raspberry Pi і операційна система 62 5.4. Операційна система робота 65 6. ОХОРОНА ПРАЦІ ТА БЕЗПЕКА В НАДЗВИЧАЙНИХ СИТУАЦІЯХ 69 6.1. Загальна характеристика приміщення і робочого місця 70 6.2. Аналіз потенційно небезпечних і шкідливих виробничих факторів на робочому місці 73 6.3. Безпека в надзвичайних ситуаціях 75 ВИСНОВКИ 78 БІБЛІОГРАФІЯ 79 ДОДАТКИ 84
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Gomes, Pedro Miguel de Barros. "LADAR based mapping and obstacle detection system for service robots." Master's thesis, Faculdade de Ciências e Tecnologia, 2010. http://hdl.handle.net/10362/4589.
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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de ComputadoresWhen travelling in unfamiliar environments, a mobile service robot needs to acquire information about his surroundings in order to detect and avoid obstacles and arrive safely at his destination. This dissertation presents a solution for the problem of mapping and obstacle detection in indoor/outdoor structured3 environments, with particular application on service robots equipped with a LADAR. Since this system was designed for structured environments, offroad terrains are outside the scope of this work. Also, the use of any a priori knowledge about LADAR’s surroundings is discarded, i.e. the developed mapping and obstacle detection system works in unknown environments. In this solution, it is assumed that the robot, which carries the LADAR and the mapping and obstacle detection system, is based on a planar surface which is considered to be the ground plane. The LADAR is positioned in a way suitable for a three dimensional world and an AHRS sensor is used to increase the robustness of the system to variations on robot’s attitude, which, in turn, can cause false positives on obstacle detection. The results from the experimental tests conducted in real environments through the incorporation on a physical robot suggest that the developed solution can be a good option for service robots driving in indoor/outdoor structured environments.
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Andreasson, Henrik. "Local visual feature based localisation and mapping by mobile robots." Doctoral thesis, Örebro : Örebro University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-2444.
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Schaefer, Alexander [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Highly accurate lidar-based mapping and localization for mobile robots." Freiburg : Universität, 2020. http://d-nb.info/1207756016/34.
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BELO, FELIPE AUGUSTO WEILEMANN. "EXPLORATION AND VISUAL MAPPING ALGORITHMS DEVELOPMENT FOR LOW COST MOBILE ROBOTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9142@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOAo mesmo tempo em que a autonomia de robôs pessoais e domésticos aumenta, cresce a necessidade de interação dos mesmos com o ambiente. A interação mais básica de um robô com o ambiente é feita pela percepção deste e sua navegação. Para uma série de aplicações não é prático prover modelos geométricos válidos do ambiente a um robô antes de seu uso. O robô necessita, então, criar estes modelos enquanto se movimenta e percebe o meio em que está inserido através de sensores. Ao mesmo tempo é necessário minimizar a complexidade requerida quanto a hardware e sensores utilizados. No presente trabalho, um algoritmo iterativo baseado em entropia é proposto para planejar uma estratégia de exploração visual, permitindo a construção eficaz de um modelo em grafo do ambiente. O algoritmo se baseia na determinação da informação presente em sub-regiões de uma imagem panorâmica 2-D da localização atual do robô obtida com uma câmera fixa sobre o mesmo. Utilizando a métrica de entropia baseada na Teoria da Informação de Shannon, o algoritmo determina nós potenciais para os quais deve se prosseguir a exploração. Através de procedimento de Visual Tracking, em conjunto com a técnica SIFT (Scale Invariant Feature Transform), o algoritmo auxilia a navegação do robô para cada nó novo, onde o processo é repetido. Um procedimento baseado em transformações invariáveis a determinadas variações espaciais (desenvolvidas a partir de Fourier e Mellin) é utilizado para auxiliar o processo de guiar o robô para nós já conhecidos. Também é proposto um método baseado na técnica SIFT. Os processos relativos à obtenção de imagens, avaliação, criação do grafo, e prosseguimento dos passos citados continua até que o robô tenha mapeado o ambiente com nível pré-especificado de detalhes. O conjunto de nós e imagens obtidos são combinados de modo a se criar um modelo em grafo do ambiente. Seguindo os caminhos, nó a nó, um robô pode navegar pelo ambiente já explorado. O método é particularmente adequado para ambientes planos. As componentes do algoritmo proposto foram desenvolvidas e testadas no presente trabalho. Resultados experimentais mostrando a eficácia dos métodos propostos são apresentados.
As the autonomy of personal service robotic systems increases so has their need to interact with their environment. The most basic interaction a robotic agent may have with its environment is to sense and navigate through it. For many applications it is not usually practical to provide robots in advance with valid geometric models of their environment. The robot will need to create these models by moving around and sensing the environment, while minimizing the complexity of the required sensing hardware. This work proposes an entropy-based iterative algorithm to plan the robot´s visual exploration strategy, enabling it to most efficiently build a graph model of its environment. The algorithm is based on determining the information present in sub-regions of a 2- D panoramic image of the environment from the robot´s current location using a single camera fixed on the mobile robot. Using a metric based on Shannon s information theory, the algorithm determines potential locations of nodes from which to further image the environment. Using a Visual Tracking process based on SIFT (Scale Invariant Feature Transform), the algorithm helps navigate the robot to each new node, where the imaging process is repeated. An invariant transform (based on Fourier and Mellin) and tracking process is used to guide the robot back to a previous node. Also, an SIFT based method is proposed to accomplish such task. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The set of nodes and the images taken at each node are combined into a graph to model the environment. By tracing its path from node to node, a service robot can navigate around its environment. This method is particularly well suited for flat-floored environments. The components of the proposed algorithm were developed and tested. Experimental results show the effectiveness of the proposed methods.
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Holz, Dirk [Verfasser]. "Efficient 3D Segmentation, Registration and Mapping for Mobile Robots / Dirk Holz." Bonn : Universitäts- und Landesbibliothek Bonn, 2017. http://d-nb.info/1139048856/34.
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Trevor, Alexander J. B. "Semantic mapping for service robots: building and using maps for mobile manipulators in semi-structured environments." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53583.
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Although much progress has been made in the field of robotic mapping, many challenges remain including: efficient semantic segmentation using RGB-D sensors, map representations that include complex features (structures and objects), and interfaces for interactive annotation of maps. This thesis addresses how prior knowledge of semi-structured human environments can be leveraged to improve segmentation, mapping, and semantic annotation of maps. We present an organized connected component approach for segmenting RGB-D data into planes and clusters. These segments serve as input to our mapping approach that utilizes them as planar landmarks and object landmarks for Simultaneous Localization and Mapping (SLAM), providing necessary information for service robot tasks and improving data association and loop closure. These features are meaningful to humans, enabling annotation of mapped features to establish common ground and simplifying tasking. A modular, open-source software framework, the OmniMapper, is also presented that allows a number of different sensors and features to be combined to generate a combined map representation, and enabling easy addition of new feature types.
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ARESTEGUI, NILTON CESAR ANCHAYHUA. "COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR VISUAL SELF-LOCALIZATION AND MAPPING OF MOBILE ROBOTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=31775@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Esta dissertação introduz um estudo sobre os algoritmos de inteligência computacional para o controle autônomo dos robôs móveis, Nesta pesquisa, são desenvolvidos e implementados sistemas inteligentes de controle de um robô móvel construído no Laboratório de Robótica da PUC-Rio, baseado numa modificação do robô ER1. Os experimentos realizados consistem em duas etapas: a primeira etapa de simulação usando o software Player-Stage de simulação do robô em 2-D onde foram desenvolvidos os algoritmos de navegação usando as técnicas de inteligência computacional; e a segunda etapa a implementação dos algoritmos no robô real. As técnicas implementadas para a navegação do robô móvel estão baseadas em algoritmos de inteligência computacional como são redes neurais, lógica difusa e support vector machine (SVM) e para dar suporte visual ao robô móvel foi implementado uma técnica de visão computacional chamado Scale Invariant Future Transform (SIFT), estes algoritmos em conjunto fazem um sistema embebido para dotar de controle autônomo ao robô móvel. As simulações destes algoritmos conseguiram o objetivo, mas na implementação surgiram diferenças muito claras respeito à simulação pelo tempo que demora em processar o microprocessador.
This theses introduces a study on the computational intelligence algorithms for autonomous control of mobile robots, In this research, intelligent systems are developed and implemented for a robot in the Robotics Laboratory of PUC-Rio, based on a modiÞcation of the robot ER1. The verification consist of two stages: the first stage includes simulation using Player-Stage software for simulation of the robot in 2-D with the developed of artiÞcial intelligence; an the second stage, including the implementation of the algorithms in the real robot. The techniques implemented for the navigation of the mobile robot are based on algorithms of computational intelligence as neural networks, fuzzy logic and support vector machine (SVM); and to give visual support to the mobile robot was implemented the visual algorithm called Scale Invariant Future Transform (SIFT), these algorithms in set makes an absorbed system to endow with independent control the mobile robot. The simulations of these algorithms had obtained the objective but in the implementation clear differences had appeared respect to the simulation, it just for the time that delays in processing the microprocessor.
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Maffei, Renan de Queiroz. "Translating sensor measurements into texts for localization and mapping with mobile robots." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/158403.
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Localização e Mapeamento Simultâneos (SLAM), fundamental para robôs dotados de verdadeira autonomia, é um dos problemas mais difíceis na Robótica e consiste em estimar a posição de um robô que está se movendo em um ambiente desconhecido, enquanto incrementalmente constrói-se o mapa de tal ambiente. Provavelmente o requisito mais importante para localização e mapeamento adequados seja um preciso reconhecimento de local, isto é, determinar se um robô estava no mesmo lugar em diferentes ocasiões apenas analizando as observações feitas pelo robô em cada ocasião. A maioria das abordagens da literatura são boas quando se utilizam sensores altamente expressivos, como câmeras, ou quando o robô está situado em ambientes com pouco ambiguidade. No entanto, este não é o caso, por exemplo, quando o robô equipado apenas com sensores de alcance está em ambientes internos estruturados altamente ambíguos. Uma boa estratégia deve ser capaz de lidar com tais ambientes, lidar com ruídos e erros nas observações e, especialmente, ser capaz de modelar o ambiente e estimar o estado do robô de forma eficiente. Nossa proposta consiste em traduzir sequências de medições de laser em uma representação de texto eficiente e compacta, para então lidar com o problema de reconhecimento de local usando técnicas de processamento lingüísticos. Nós traduzimos as medições dos sensores em valores simples computados através de um novo modelo de observação baseado em estimativas de densidade de kernel chamado de Densidade de Espaço Livre (FSD). Estes valores são quantificados permitindo a divisão do ambiente em regiões contíguas de densidade homogênea, como corredores e cantos. Regiões são representadas de forma compacta por simples palavras descrevendo o valor de densidade espacial, o tamanho e a variação da orientação daquela região. No final, as cadeias de palavras compõem um texto, no qual se buscam casamentos de n-gramas (isto é, sequências de palavras). Nossa técnica também é aplicada com sucesso em alguns cenários de operação de longo-prazo, onde devemos lidar com objetos semi-estáticos (i.e. que se movem ocasionalmente, como portas e mobílias). Todas as abordagens foram avaliadas em cenários simulados e reais obtendo-se bons resultados.Simultaneous Localization and Mapping (SLAM), fundamental for building robots with true autonomy, is one of the most difficult problems in Robotics and consists of estimating the position of a robot that is moving in an unknown environment while incrementally building the map of such environment. Arguably the most crucial requirement to obtain proper localization and mapping is precise place recognition, that is, determining if the robot is at the same place in different occasions just by looking at the observations taken by the robot. Most approaches in literature are good when using highly expressive sensors such as cameras or when the robot is situated in low ambiguous environments. However this is not the case, for instance, using robots equipped only with range-finder sensors in highly ambiguous indoor structured environments. A good SLAM strategy must be able to handle these scenarios, deal with noise and observation errors, and, especially, model the environment and estimate the robot state in an efficient way. Our proposal in this work is to translate sequences of raw laser measurements into an efficient and compact text representation and deal with the place recognition problem using linguistic processing techniques. First, we translate raw sensor measurements into simple observation values computed through a novel observation model based on kernel-density estimation called Free-Space Density (FSD). These values are quantized into significant classes allowing the division of the environment into contiguous regions of homogeneous spatial density, such as corridors and corners. Regions are represented in a compact form by simple words composed of three syllables – the value of spatial density, the size and the variation of orientation of that region. At the end, the chains of words associated to all observations made by the robot compose a text, in which we search for matches of n-grams (i.e. sequences of words), which is a popular technique from shallow linguistic processing. The technique is also successfully applied in some scenarios of long-term operation, where we must deal with semi-static objects (i.e. that can move occasionally, such as doors and furniture). All approaches were evaluated in simulated and real scenarios obtaining good results.