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Published: 14 March 2023

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1

Agarwal, Pratik [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Robust graph-based localization and mapping = Robuste Graph-basierte Lokalisierung und Kartierung." Freiburg : Universität, 2015. http://d-nb.info/111980549X/34.

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2

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

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SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently. Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers . In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far. The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem’s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets. This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.
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3

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

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SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently. Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers. In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far. The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem\'s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets. This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.
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4

Jama, Michal. "Monocular vision based localization and mapping." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/8561.

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Doctor of Philosophy
Department of Electrical and Computer Engineering
Balasubramaniam Natarajan
Dale E. Schinstock
In this dissertation, two applications related to vision-based localization and mapping are considered: (1) improving navigation system based satellite location estimates by using on-board camera images, and (2) deriving position information from video stream and using it to aid an auto-pilot of an unmanned aerial vehicle (UAV). In the first part of this dissertation, a method for analyzing a minimization process called bundle adjustment (BA) used in stereo imagery based 3D terrain reconstruction to refine estimates of camera poses (positions and orientations) is presented. In particular, imagery obtained with pushbroom cameras is of interest. This work proposes a method to identify cases in which BA does not work as intended, i.e., the cases in which the pose estimates returned by the BA are not more accurate than estimates provided by a satellite navigation systems due to the existence of degrees of freedom (DOF) in BA. Use of inaccurate pose estimates causes warping and scaling effects in the reconstructed terrain and prevents the terrain from being used in scientific analysis. Main contributions of this part of work include: 1) formulation of a method for detecting DOF in the BA; and 2) identifying that two camera geometries commonly used to obtain stereo imagery have DOF. Also, this part presents results demonstrating that avoidance of the DOF can give significant accuracy gains in aerial imagery. The second part of this dissertation proposes a vision based system for UAV navigation. This is a monocular vision based simultaneous localization and mapping (SLAM) system, which measures the position and orientation of the camera and builds a map of the environment using a video-stream from a single camera. This is different from common SLAM solutions that use sensors that measure depth, like LIDAR, stereoscopic cameras or depth cameras. The SLAM solution was built by significantly modifying and extending a recent open-source SLAM solution that is fundamentally different from a traditional approach to solving SLAM problem. The modifications made are those needed to provide the position measurements necessary for the navigation solution on a UAV while simultaneously building the map, all while maintaining control of the UAV. The main contributions of this part include: 1) extension of the map building algorithm to enable it to be used realistically while controlling a UAV and simultaneously building the map; 2) improved performance of the SLAM algorithm for lower camera frame rates; and 3) the first known demonstration of a monocular SLAM algorithm successfully controlling a UAV while simultaneously building the map. This work demonstrates that a fully autonomous UAV that uses monocular vision for navigation is feasible, and can be effective in Global Positioning System denied environments.
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5

Cummins, Mark. "Probabilistic localization and mapping in appearance space." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:a34370f2-a2a9-40b5-9a2d-1c8c616ff07a.

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This thesis is concerned with the problem of place recognition for mobile robots. How can a robot determine its location from an image or sequence of images, without any prior knowledge of its position, even in a world where many places look identical? We outline a new probabilistic approach to the problem, which we call Fast Appearance Based Mapping or FAB-MAP. Our map of the environment consists of a set of discrete locations, each with an associated appearance model. For every observation collected by the robot, we compute a probability distribution over the map, and either create a new location or update our belief about the appearance of an existing location. The technique can be seen as a new type of SLAM algorithm, where the appearance of locations (rather than their position) is subject to estimation. Unlike existing SLAM systems, our appearance based technique does not rely on keeping track of the robot in any metric coordinate system. Thus it is applicable even when informative observations are available only intermittently. Solutions to the loop closure detection problem, the kidnapped robot problem and the multi-session mapping problem arise as special cases of our general approach. Abstract Our probabilistic model introduces several technical advances. The model incorporates correlations between visual features in a novel way, which is shown to improve system performance. Additionally, we explicitly compute an approximation to the partition function in our Bayesian formulation, which provides a natural probabilistic measure of when a new observation should be assigned to a location not already present in the map. The technique is applicable even in visually repetitive environments where many places look the same. Abstract Finally, we define two distinct approximate inference procedures for the model. The first of these is based on concentration inequalities and has general applicability beyond the problem considered in this thesis. The second approach, built on inverted index techniques, is tailored to our specific problem of place recognition, but achieves extreme efficiency, allowing us to apply FAB-MAP to navigation problems on the largest scale. The thesis concludes with a visual SLAM experiment on a trajectory 1,000 km long. The system successfully detects loop closures with close to 100% precision and requires average inference time of only 25 ms by the end of the trajectory.
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6

Lim, Yu-Xi. "Efficient wireless location estimation through simultaneous localization and mapping." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28219.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Owen, Henry; Committee Member: Copeland, John; Committee Member: Giffin, Jonathon; Committee Member: Howard, Ayanna; Committee Member: Riley, George.
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7

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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8

Oliveira, Douglas Coelho Braga de. "Dynamic-object-aware simultaneous localization and mapping for augmented reality applications." Universidade Federal de Juiz de Fora (UFJF), 2018. https://repositorio.ufjf.br/jspui/handle/ufjf/8059.

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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Realidade Aumentada (RA) é uma tecnologia que permite combinar objetos virtuais tridimensionais com um ambiente predominantemente real, de forma a construir um novo ambiente onde os objetos reais e virtuais podem interagir uns com os outros em tempo real. Para fazer isso, é necessário encontrar a pose do observador (câmera, HMD, óculos inteligentes, etc.) em relação a um sistema de coordenadas global. Geralmente, algum objeto físico conhecido é usado para marcar o referencial para as projeções e para a posição do observador. O problema de Localização e Mapeamento Simultâneo (SLAM) se origina da comunidade de robótica como uma condição necessária para se construir robôs verdadeiramente autônomos, capazes de se auto localizarem em um ambiente desconhecido ao mesmo tempo que constroem um mapa da cena observada a partir de informações capturadas por um conjunto de sensores. A principal contribuição do SLAM para a RA é permitir aplicações em ambientes despreparados, ou seja, sem marcadores. No entanto, ao eliminar o marcador, perdemos o referencial para a projeção dos objetos virtuais e a principal fonte de interação entre os elementos reais e virtuais. Embora o mapa gerado possa ser processado a fim de encontrar uma estrutura conhecida, como um plano predominante, para usá-la como referencial, isso ainda não resolve a questão das interações. Na literatura recente, encontramos trabalhos que integram um sistema de reconhecimento de objetos ao SLAM e incorporam tais objetos ao mapa. Frequentemente, assume-se um mapa estático, devido às limitações das técnicas envolvidas, de modo que o objeto é usado apenas para fornecer informações semânticas sobre a cena. Neste trabalho, propomos um novo framework que permite estimar simultaneamente a posição da câmera e de objetos para cada quadro de vídeo em tempo real. Dessa forma, cada objeto é independente e pode se mover pelo mapa livremente, assim como nos métodos baseados em marcadores, mas mantendo as vantagens que o SLAM fornece. Implementamos a estrutura proposta sobre um sistema SLAM de última geração a fim de validar nossa proposta e demonstrar a potencial aplicação em Realidade Aumentada.
Augmented Reality (AR) is a technology that allows combining three-dimensional virtual objects with an environment predominantly real in a way to build a new environment where both real and virtual objects can interact with each other in real-time. To do this, it is required to nd the pose of the observer (camera, HMD, smart glasses etc) in relation to a global coordinate system. Commonly, some well known physical object, called marker, is used to de ne the referential for both virtual objects and the observer's position. The Simultaneous Localization and Mapping (SLAM) problem borns from robotics community as a way to build truly autonomous robots by allowing they to localize themselves while they build a map of the observed scene from the input data of their coupled sensors. SLAM-based Augmented Reality is an active and evolving research line. The main contribution of the SLAM to the AR is to allow applications on unprepared environments, i.e., without markers. However, by eliminating the marker object, we lose the referential for virtual object projection and the main source of interaction between real and virtual elements. Although the generated map can be processed in order to nd a known structure, e.g. a predominant plane, to use it as the referential system, this still not solve for interactions. In the recent literature, we can found works that integrate an object recognition system to the SLAM in a way the objects are incorporated into the map. The SLAM map is frequently assumed to be static, due to limitations on techniques involved, so that on these works the object is just used to provide semantic information about the scene. In this work, we propose a new framework that allows estimating simultaneously the camera and object positioning for each camera image in real time. In this way, each object is independent and can move through the map as well as in the marker-based methods but with the SLAM advantages kept. We develop our proposed framework over a stateof- the-art SLAM system in order to evaluate our proposal and demonstrate potentials application in Augmented Reality.
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9

Lee, Chun-Fan Computer Science &amp Engineering Faculty of Engineering UNSW. "Towards topological mapping with vision-based simultaneous localization and map building." Awarded by:University of New South Wales. Computer Science & Engineering, 2008. http://handle.unsw.edu.au/1959.4/41551.

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Although the theory of Simultaneous Localization and Map Building (SLAM) is well developed, there are many challenges to overcome when incorporating vision sensors into SLAM systems. Visual sensors have different properties when compared to range finding sensors and therefore require different considerations. Existing vision-based SLAM algorithms extract point landmarks, which are required for SLAM algorithms such as the Kalman filter. Under this restriction, the types of image features that can be used are limited and the full advantages of vision not realized. This thesis examines the theoretical formulation of the SLAM problem and the characteristics of visual information in the SLAM domain. It also examines different representations of uncertainty, features and environments. It identifies the necessity to develop a suitable framework for vision-based SLAM systems and proposes a framework called VisionSLAM, which utilizes an appearance-based landmark representation and topological map structure to model metric relations between landmarks. A set of Haar feature filters are used to extract image structure statistics, which are robust against illumination changes, have good uniqueness property and can be computed in real time. The algorithm is able to resolve and correct false data associations and is robust against random correlation resulting from perceptual aliasing. The algorithm has been tested extensively in a natural outdoor environment.
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10

Droeschel, David Marcel [Verfasser]. "Efficient Methods for Lidar-based Mapping and Localization / David Marcel Droeschel." Bonn : Universitäts- und Landesbibliothek Bonn, 2020. http://d-nb.info/122166929X/34.

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11

LYRIO, JUNIOR L. J. "Image-Based Mapping and Localization using VG-RAM Weightless Neural Networks." Universidade Federal do Espírito Santo, 2014. http://repositorio.ufes.br/handle/10/4269.

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Localização e Mapeamento são problemas fundamentais da robótica autônoma. Robôs autônomos necessitam saber onde se encontram em sua área de operação para navegar pelo ambiente e realizar suas atividades de interesse. Neste trabalho, apresentamos um sistema para mapeamento e localização baseado em imagens que emprega Redes Neurais Sem Peso do Tipo VG-RAM (RNSP VG-RAM) para um carro autônomo. No nosso sistema, uma RNSP VG-RAM aprende posições globais associadas à imagens e marcos tridimensionais capturados ao longo de uma trajetória, e constrói um mapa baseado nessas informações. Durante a localização, o sistema usa um Filtro Estendido de Kalman para integrar dados de sensores e do mapa ao longo do tempo, através de passos consecutivos de predição e correção do estado do sistema. O passo de predição é calculado por meio do modelo de movimento do nosso robô, que utiliza informações de velocidade e ângulo do volante, calculados a partir de imagens utilizando-se odometria visual. O passo de correção é realizado através da integração das posições globais que a RNSP VG-RAM com a correspondência dos marcos tridimensional previamente armazenados no mapa do robô. Realizamos experimentos com o nosso sistema usando conjuntos de dados do mundo real. Estes conjuntos de dados consistem em dados provenientes de vários sensores de um carro autônomo, que foram sistematicamente adquiridos durante voltas ao redor do campus principal da UFES (um circuito de 3,57 km). Nossos resultados experimentais mostram que nosso sistema é capaz de aprender grandes mapas (vários quilômetros de comprimento) e realizar a localização global e rastreamento de posição de carros autônomos, com uma precisão de 0,2 metros quando comparado à abordagem de Localização de Monte Carlo utilizado no nosso veículo autônomo.
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12

Bertoletti, Michele. "Appearence-based Visual Localization for Indoor Navigation of Quadrotors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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The main idea behind this work is to perform mapping and localization for a quadrotor in an indoor environment topologically represented by a set of ordered key images acquired in a prior learning phase. The mapping and localization are based on a feature matching between the most recently acquired image and the set of key images. The overall task is composed of two consecutive phases: a mapping phase and a navigation phase. The mapping phase is an offline learning phase during which the quadrotor is manually moved around the environment by means of a joystick. The drone acquires a sequence of images that will represent the path the quadrotor should follow in the next phase. The navigation phase is accomplished online and it’s composed by an initial localization, a successive localization and the motion control. The quadrotor can start from any initial position in the environment and the initial localization algorithm determines the initial pose of the quadrotor by comparing the first acquired image with all the key images from the prior phase and selecting the two images of the path most similar to the acquired image. The successive localization algorithm is used, during the motion of the quadrotor, to decide when to switch the reference key images. This step is necessary in order to determine the position of the quadrotor during the motion. Finally, the control law is built to minimize the difference between the desired feature value and the current feature value, in order to keep the quadrotor on the defined trajectory.
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13

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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Ferrin, Jeffrey L. "Autonomous Goal-Based Mapping and Navigation Using a Ground Robot." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6190.

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Ground robotic vehicles are used in many different applications. Many of these uses include tele-operation of the robot. This allows the robot to be deployed in locations that are too difficult or are unsafe for human access. The ability of a ground robot to autonomously navigate to a desired location without a-priori map information and without using GPS would allow robotic vehicles to be used in many of these situations and would free the operator to focus on other more important tasks. The purpose of this research is to develop algorithms that enable a ground robot to autonomously navigate to a user-selected location. The goal is selected from a video feed from the robot and the robot drives to the goal location while avoiding obstacles. The method uses a monocular camera for measuring the locations of the goal and landmarks. The method is validated in simulation and through experiments on an iRobot Packbot platform. A novel goal-based robocentric mapping algorithm is derived in Chapter 3. This map is created using an extended Kalman filter (EKF) by tracking the position of the goal along with other available landmarks surrounding the robot as it drives towards the goal. The mapping is robocentric, meaning that the map is a local map created in the robot-body frame. A unique state definition of the goal states and additional landmarks is presented that improves the estimate of the goal location. An improved 3D model is derived and used to allow the robot to drive on non-flat terrain while calculating the position of the goal and other landmarks. The observability and consistency of the proposed method are shown in Chapter 4. The visual tracking algorithm is explained in Chapter 5. This tracker is used with the EKF to improve tracking performance and to allow the objects to be tracked even after leaving the camera field of view for significant periods of time. This problem presents a difficult challenge for visual tracking because of the drastic change in size of the goal object as the robot approaches the goal. The tracking method is validated through experiments in real-world scenarios. The method of planning and control is derived in Chapter 6. A Model Predictive Control (MPC) formulation is designed that explicitly handles the sensor constraints of a monocular camera that is rigidly mounted to the vehicle. The MPC uses an observability-based cost function to drive the robot along a path that minimizes the position error of the goal in the robot-body frame. The MPC algorithm also avoids obstacles while driving to the goal. The conditions are explained that guarantee the robot will arrive within some specified distance of the goal. The entire system is implemented on an iRobot Packbot and experiments are conducted and presented in Chapter 7. The methods described in this work are shown to work on actual hardware allowing the robot to arrive at a user-selected goal in real-world scenarios.
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15

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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16

Fossel, J. "Improving light detection and ranging based simultaneous localization and mapping with advanced map representations." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3022937/.

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Svensson, Depraetere Xavier. "Application of new particle-based solutions to the Simultaneous Localization and Mapping (SLAM) problem." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212999.

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In this thesis, we explore novel solutions to the Simultaneous Localization and Mapping (SLAM) problem based on particle filtering and smoothing methods. In essence, the SLAM problem constitutes of two interdependent tasks: map building and tracking. Three solution methods utilizing different smoothing techniques are explored. The smoothing methods used are fixed lag smoothing (FLS), forward-only forward-filtering backward-smoothing (forward-only FFBSm) and the particle-based, rapid incremental smoother (PaRIS). In conjunction with these smoothing techniques the well-established Expectation-Maximization (EM) algorithm is used to produce maximum-likelihood estimates of the map. The three solution method are then evaluated and compared in a simulated setting.
I detta examensarbete utforskas nya lösningar till Simultaneous Localization and Mapping (SLAM) problemet baserat på partikelfilter- och partikelglättnings-metoder. I sin grund består SLAM problemet av två av varandra beroende uppgifter: kartläggning och spårning. Tre lösningsmetoder som använder olika glättnings-metoder appliceras för att lösa dessa uppgifter.  Dessa glättningsmetoder är fixed lag smoothing (FLS), forward-only forward-filtering backward-smoothing (forward-only FFBSm) och the particle-based, rapid incremental smoother (PaRIS). I samband med dessa glättningstekniker används den väletablerade Expectation-Maximization (EM) algoritmen för att skapa maximum-likelihood skattningar av kartan. De tre lösningsmetoderna utvärderas sedan och jämförs i en simulerad miljö.
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Yilmaz, Özgün. "Infrared based monocular relative navigation for active debris removal." Thesis, Cranfield University, 2018. http://dspace.lib.cranfield.ac.uk/handle/1826/13727.

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In space, visual based relative navigation systems suffer from the harsh illumination conditions of the target (e.g. eclipse conditions, solar glare, etc.). In current Rendezvous and Docking (RvD) missions, most of these issues are addressed by advanced mission planning techniques (e.g strict manoeuvre timings). However, such planning would not always be feasible for Active Debris Removal (ADR) missions which have more unknowns. Fortunately, thermal infrared technology can operate under any lighting conditions and therefore has the potential to be exploited in the ADR scenario. In this context, this study investigates the benefits and the challenges of infrared based relative navigation. The infrared environment of ADR is very much different to that of terrestrial applications. This study proposes a methodology of modelling this environment in a computationally cost effective way to create a simulation environment in which the navigation solution can be tested. Through an intelligent classification of possible target surface coatings, the study is generalised to simulate the thermal environment of space debris in different orbit profiles. Through modelling various scenarios, the study also discusses the possible challenges of the infrared technology. In laboratory conditions, providing the thermal-vacuum environment of ADR, these theoretical findings were replicated. By use of this novel space debris set-up, the study investigates the behaviour of infrared cues extracted by different techniques and identifies the issue of short-lifespan features in the ADR scenarios. Based on these findings, the study suggests two different relative navigation methods based on the degree of target cooperativeness: partially cooperative targets, and uncooperative targets. Both algorithms provide the navigation solution with respect to an online reconstruction of the target. The method for partially cooperative targets provides a solution for smooth trajectories by exploiting the subsequent image tracks of features extracted from the first frame. The second algorithm is for uncooperative targets and exploits the target motion (e.g. tumbling) by formulating the problem in terms of a static target and a moving map (i.e. target structure) within a filtering framework. The optical flow information is related to the target motion derivatives and the target structure. A novel technique that uses the quality of the infrared cues to improve the algorithm performance is introduced. The problem of short measurement duration due to target tumbling motion is addressed by an innovative smart initialisation procedure. Both navigation solutions were tested in a number of different scenarios by using computer simulations and a specific laboratory set-up with real infrared camera. It is shown that these methods can perform well as the infrared-based navigation solutions using monocular cameras where knowledge relating to the infrared appearance of the target is limited.
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BONTEMPO, ALAN PORTO. "A HYBRID APPROACH FOR SIMULTANEOUS LOCALIZATION AND MAPPING WITH SONAR BASED ROBOTS AND EXTENDED KALMAN FILTER." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2012. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=21009@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
PROGRAMA DE EXCELENCIA ACADEMICA
Este trabalho aborda o problema da Localização e Mapeamento Simultâneos em ambientes estruturados, utilizando um robô móvel equipado com sonares, bússola eletrônica e encoders. Na modelagem sugerida há a construção do mapa do ambiente e a localização do robô de forma interativa. O método proposto, denominado de LMS-H (Localização e Mapeamento Simultâneos - Híbrido), faz uso de duas formas de representação do ambiente: Mapa de Ocupação em Grade e Representação Contínua. O Mapa de Ocupação em Grade divide o ambiente em pequenas partes iguais, classificando-as em ocupadas ou vazias. A Representação Contínua utiliza retas para representar os planos detectados no ambiente, formando um mapa em duas dimensões e cada reta do mapa é considerada um marco. Sempre que um plano é novamente detectado pelo robô a reta correspondente a ele é recalculada com os novos pontos obtidos e a posição do robô é atualizada via Filtro de Kalman Estendido. A eficácia do método foi comprovada através de seis estudos de caso: três em ambientes virtuais e três em ambientes reais. Os estudos de casos em ambientes reais foram realizados utilizando-se um protótipo feito sob a plataforma LEGO Mindstorms. Os resultados obtidos comprovaram a eficácia do método proposto.
This work addresses the problem of Simultaneous Localization and Mapping in structured environments using a mobile robot equipped with sonar, electronic compass and encoders. In the proposed modeling there are the construction of the environment map and the robot localization interactively. The proposed method, called H-SLAM (Hybrid - Simultaneous Localization and Mapping), makes use kinds of environment representation: Occupancy Grid Map and Continuous Representation. The Occupancy Grid Map divides the environment into small equal parts, and classifies it as occupied or empty. The Continuous Representation uses lines to represent detected planes in the environment, forming a two-dimensional map. Each line of the map is considered a landmark. Every time a plan is redetected by the robot the corresponding line to it is rebuild with the new points obtained and the robot s position is updated through Extended Kalman Filter. The model effectiveness was proved with computer simulations in three virtual environments. Using a prototype developed with LEGO Mindstorms platform three other experiments were also performed in real environments. The results demonstrated the effectiveness of the proposed method.
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20

Tweddle, Brent Edward. "Computer vision-based localization and mapping of an unknown, uncooperative and spinning target for spacecraft proximity operations." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85693.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 399-410).
Prior studies have estimated that there are over 100 potential target objects near the Geostationary Orbit belt that are spinning at rates of over 20 rotations per minute. For a number of reasons, it may be desirable to operate in close proximity to these objects for the purposes of inspection, docking and repair. Many of them have an unknown geometric appearance, are uncooperative and non-communicative. These types of characteristics are also shared by a number of asteroid rendezvous missions. In order to safely operate in close proximity to an object in space, it is important to know the target object's position and orientation relative to the inspector satellite, as well as to build a three-dimensional geometric map of the object for relative navigation in future stages of the mission. This type of problem can be solved with many of the typical Simultaneous Localization and Mapping (SLAM) algorithms that are found in the literature. However, if the target object is spinning with signicant angular velocity, it is also important to know the linear and angular velocity of the target object as well as its center of mass, principal axes of inertia and its inertia matrix. This information is essential to being able to propagate the state of the target object to a future time, which is a key capability for any type of proximity operations mission. Most of the typical SLAM algorithms cannot easily provide these types of estimates for high-speed spinning objects. This thesis describes a new approach to solving a SLAM problem for unknown and uncooperative objects that are spinning about an arbitrary axis. It is capable of estimating a geometric map of the target object, as well as its position, orientation, linear velocity, angular velocity, center of mass, principal axes and ratios of inertia. This allows the state of the target object to be propagated to a future time step using Newton's Second Law and Euler's Equation of Rotational Motion, and thereby allowing this future state to be used by the planning and control algorithms for the target spacecraft. In order to properly evaluate this new approach, it is necessary to gather experi
by Brent Edward Tweddle.
Ph. D.
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21

McVicker, William D. "Mapping and Visualizing Ancient Water Storage Systems with an ROV -- An Approach Based on Fusing Stationary Scans within a Particle Filter." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/885.

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This paper presents a new method for constructing 2D maps of enclosed un- derwater structures using an underwater robot equipped with only a 2D scanning sonar, compass and depth sensor. In particular, no motion model or odometry is used. To accomplish this, a two step offline SLAM method is applied to a set of stationary sonar scans. In the first step, the change in position of the robot between each consecutive pair of stationary sonar scans is estimated using a particle filter. This set of pair wise relative scan positions is used to create an estimate of each scan’s position within a global coordinate frame using a weighted least squares fit that optimizes consistency between the relative positions of the entire set of scans. In the second step of the method, scans and their estimated positions act as inputs to a mapping algorithm that constructs 2D octree-based evidence grid maps of the site. This work is motivated by a multi-year archaeological project that aims to construct maps of ancient water storage systems, i.e. cisterns, on the islands of Malta and Gozo. Cisterns, wells, and water galleries within fortresses, churches and homes operated as water storage systems as far back as 2000 B.C. Using a Remotely Operated Vehicle (ROV) these water storage systems located around the islands were explored while collecting video, still images, sonar, depth, and compass measurements. Data gathered from 5 different expeditions has produced maps of over 90 sites. Presented are results from applying the new mapping method to both a swimming pool of known size and to several of the previously unexplored water storage systems.
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22

García, Fidalgo Emilio. "Appearance-based Loop Closure Detection and its Application to Topological Mapping and Image Mosaicing." Doctoral thesis, Universitat de les Illes Balears, 2016. http://hdl.handle.net/10803/399587.

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Mapping and localization are two essential processes in autonomous mobile robotics since they are the basis of other higher level and more complex tasks, such as obstacle avoidance or path planning. Mapping is the process through which a robot builds its own representation of the environment when a map of the environment is not available. There exist mainly two types of maps: metric and topological. While metric maps represent the world as accurate as possible with regard to a global coordinate system, topological maps represent the environament in an abstract manner by means of a graph, which implies several benefits in front of the classic metric approaches. Due to the unavoidable noise that sensors present, mapping algorithms usually rely on loop closure detection techniques, which entails the correct identification of previously seen places to reduce the uncertainty of the resulting maps. This dissertation deals with the problem of generating topological maps of the environment using efficient appearance-based loop closure detection techniques. Since the quality of a visual loop closure detection algorithm is related to the image description method and its ability to index previously seen images, several methods for loop closure detection adopting diferent approaches are developed and assessed. Then, these methods are used as basic components in three novel topological mapping algorithms. The results obtained indicate that the solutions proposed attain a better performance than several state-of-the-art approaches. To conclude, given that place recognition is also a key component in other research areas, a multi-threaded image mosaicing algorithm is proposed. This approach makes use of one of the loop closure detection techniques previously introduced in order to find overlapping pairs between images and finally obtain seamless mosaics of different environments in a reasonable amount of time.
El mapeo y la localización son dos procesos fundamentales en robótica autónoma móvil debido a que son la base de otras tareas de más alto nivel y más complejas, como la evitación de obstáculos o la planificación de rutas. El mapeo es el proceso a través del cual el robot construye su propia representación del entorno cuando el mapa no está disponible. Existen fundamentalmente dos tipos de mapas: los métricos y los topológicos. Mientras que los mapas métricos representan el mundo lo más exacto posible de acuerdo a un sistema de coordenadas de referencia, los mapas topológicos lo representan de un modo abstracto utilizando un grafo, lo que supone una serie de ventajas respecto a los métricos. Debido al inevitable ruido que incluyen los sensores, los algoritmos de mapeo normalmente están basados en técnicas de detección de bucles, que consisten en identificar correctamente cuando el vehículo ha vuelto a un lugar previamente visitado para reducir la incertidumbre en los mapas resultantes. Esta tesis trata de dar solución al problema de generar mapas topológicos del entorno utilizando algoritmes eficientes de detección de bucles basados en visión. Debido a que la calidad de un algoritmo de detección visual de bucles está directamente relacionada con la descripción de las imágenes y con el método utilizado para indexarlas, en esta tesis se proponen varias técnicas para detectar bucles, adoptando diferentes enfoques. Estos métodos se utilizan como Componentes básicos en tres novedosos algoritmos de mapeo topológico. Los resultados obtenidos indican que las soluciones propuestas presentan un mejor rendimiento que diversos algoritmos considerados como estado del arte por la comunidad. Para concluir, y dado que el reconocimiento de escenas es también un componente esencial en otras áreas de investigación, se presenta un algoritmo de generación de mosaicos de imágenes. Este algoritmo utiliza una de las técnicas de detección de bucles presentadas previamente para encontrar pares de imágenes que se solapan y se utiliza para obtener mosaicos en diferentes entornos en un tiempo razonable.
El mapeig i la localització són dos processos fonamentals en robòtica autònoma mòbil a causa de que són la base d’altres tasques de més alt nivell i més complexes, com l’evitació d’obstacles o la planificació de rutes. El mapeig és el procés a través del qual el robot construeix la seva pròpia representació de l’entorn quan el mapa no està disponible. Existeixen fonamentalment dos tipus de mapes: els mètrics i els topològics. Mentre que els mapes mètrics representen el món el més exacte possible d’acord a un sistema de coordenades de referència, els mapes topològics el representen d’una manera abstracte utilitzant un graf, el que suposa una sèrie d’avantatges respecte als mètrics. A causa de l’inevitable soroll que inclouen els sensors, els algorismes de mapeig normalment estan basats en tècniques de detecció de bucles, que consisteixen en identificar correctament quan el vehicle ha tornat a un lloc prèviament visitat per reduir la incertesa en els mapes resultants. Aquesta tesi tracta de donar solució al problema de generar mapes topològics de l’entorn utilitzant algorismes eficients de detecció de bucles basats en visió. Degut a que la qualitat d’un algorisme de detecció visual de bucles està directament relacionada amb la descripció de les imatges i amb el mètode utilitzat per indexarles, en aquesta tesi es proposen diverses tècniques per detectar bucles adoptant diferents enfocs. Aquests mètodes s’utilitzen com a components bàsics en tres nous algorismes de mapeig topològic. Els resultats obtinguts indiquen que les solucions proposades presenten un millor rendiment que diversos algorismes considerats com estat de l’art per la comunitat. Per concloure, i atès que el reconeixement d’escenes és també un component essencial en altres àrees d’investigació, es presenta un algorisme de generació de mosaics d’imatges. Aquest algorisme utilitza una de les tècniques de detecció de bucles presentades prèviament per trobar parells d’imatges que es solapen, i s’utilitza per obtenir mosaics en diferents entorns en un temps raonable.
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23

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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Murphy, Timothy Charles. "Examining the Effects of Key Point Detector and Descriptors on 3D Visual SLAM." Ohio University Honors Tutorial College / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1461320700.

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25

Bruce, Jake. "Learning from limited experience: Real-world robot navigation from a single traversal." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205492/1/Jacob_Bruce_Thesis.pdf.

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If you want to do something, first you have to go somewhere. Navigation is a crucial capability for any intelligent agent that is expected to operate in the world, be they human, animal, or robot. In this work, we present techniques for teaching robots to navigate in the real world, given only a single prior traversal of the environment. We employ an online adaptive approach to sequence-based localization, and a novel framework for model-based deep reinforcement learning, to develop robot systems that can successfully localize and navigate in a city.
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26

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.

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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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27

Huang, Henry. "Bearing-only SLAM : a vision-based navigation system for autonomous robots." Queensland University of Technology, 2008. http://eprints.qut.edu.au/28599/.

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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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28

Cheng, Sibo. "Error covariance specification and localization in data assimilation with industrial application Background error covariance iterative updating with invariant observation measures for data assimilation A graph clustering approach to localization for adaptive covariance tuning in data assimilation based on state-observation mapping Error covariance tuning in variational data assimilation: application to an operating hydrological model." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST067.

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Les méthodes d’assimilation de données et plus particulièrement les méthodes variationnelles sont mises à profit dans le domaine industriel pour deux grands types d’applications que sont la reconstruction de champ physique et le recalage de paramètres. Une des difficultés de mise en œuvre des algorithmes d’assimilation est que la structure de matrices de covariance d’erreurs, surtout celle d’ébauche, n’est souvent pas ou mal connue. Dans cette thèse, on s’intéresse à la spécification et la localisation de matrices de covariance dans des systèmes multivariés et multidimensionels, et dans un cadre industriel. Dans un premier temps, on cherche à adapter/améliorer notre connaissance sur les covariances d’analyse à l’aide d’un processus itératif. Dans ce but nous avons développé deux nouvelles méthodes itératives pour la construction de matrices de covariance d’erreur d’ébauche. L’efficacité de ces méthodes est montrée numériquement en expériences jumelles avec des erreurs indépendantes ou relatives aux états vrais. On propose ensuite un nouveau concept de localisation pour le diagnostic et l’amélioration des covariances des erreurs. Au lieu de s’appuyer sur une distance spatiale, cette localisation est établie exclusivement à partir de liens entre les variables d’état et les observations. Finalement, on applique une combinaison de ces nouvelles approches et de méthodes plus classiques existantes, pour un modèle hydrologique multivarié développé à EDF. L’assimilation de données est mise en œuvre pour corriger la quantité de précipitation observée afin d’obtenir une meilleure prévision du débit d’une rivière en un point donné
Data assimilation techniques are widely applied in industrial problems of field reconstruction or parameter identification. The error covariance matrices, especially the background matrix in data assimilation are often difficult to specify. In this thesis, we are interested in the specification and localization of covariance matrices in multivariate and multidimensional systems in an industrial context. We propose to improve the covariance specification by iterative processes. Hence, we developed two new iterative methods for background matrix recognition. The power of these methods is demonstrated numerically in twin experiments with independent errors or relative to true states. We then propose a new concept of localization and applied it for error covariance tuning. Instead of relying on spatial distance, this localization is established purely on links between state variables and observations. Finally, we apply these new approaches, together with other classical methods for comparison, to a multivariate hydrological model. Variational assimilation is implemented to correct the observed precipitation in order to obtain a better river flow forecast
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29

HENELL, DANIEL. "Airborne SLAM Using High-Speed Vision : The construction of a fully self-contained micro air vehicle localized and controlledusing computer vision based Simultaneous Localization And Mapping." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-141772.

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A helicopter platform was built where the all the controls, localization and other calculations are performed onboard the helicopter making it fully self-contained. The localization is made only by using a monocular camera (with an option to use a stereo pair for easier initialization) and processing the video feed with computer vision algorithms. The helicopter’s pose is estimated by a computer vision algorithm which is an extended version of PTAM, a Simultaneous Localization and Mapping (SLAM) algorithm published 2007 by G. Klein and D. Murray. The program was changed to be able to track different kinds of self-similar ground textures and to be integrated with the helicopter hardware. The algorithm was also modified to be able to auto-initialize and to keep the map size constant by pruning out far away key frames to not be confined only to small areas. The impact on tracking using high-speed vision at 60 Hz was investigated and compared to tracking at 30 Hz, respectively 10 Hz. The impact was not as big as hypothesized. Tracking stability increases a lot when going from 10 Hz to 30 Hz video. However increasing the frame rate from 30 Hz to 60 Hz has a very small effect. In 60 Hz the difference between frames becomes smaller, but does not seem to affect the tracking stability very much. The reason for this is most likely that 30 Hz is adequate for the velocities in which the helicopter flies and the limiting factor is the algorithm in itself that it cannot track in every possible setting, and this will not be fixed by increasing the frame rate further but will require changes in the algorithm. The computer vision localization works well as long as there are good salient features to track. The tracking accuracy in such cases is measured to have a RMS error of 2.4 cm compared to motion capture data that can be assumed to be ground truth.
En helikopterplattform har satts samman där all motorstyrning, lokalisering och övriga beräkningar sker ombord på helikoptern vilket gör den oberoende av extern hårdvara. Lokaliseringen av helikoptern görs enbart med hjälp av en monokulär kamera genom att analysera videoströmmen med hjälp av datorseende-algoritmer. Algoritmen som används är en anpassning av PTAM, en Similtaneous Localization and Mapping (SLAM) algoritm publicerad 2007 av G. Klein och D. Murray. Algoritmen har modifierats så att den kan bättre hantera situationer med repeterande marktextur utan speciellt utmärkande särdrag, samt att programvaran har integrerats med helikopterhårdvaran för att att skicka styrsignaler. Algoritmen har även förbättrats med automatiskt initiering av trackern, samt ett alternativ att hålla kartstorleken konstant så att helikoptern kan röra sig över större områden utan att begränsas av den ökande beräkningstiden och minnesanvändningen för en större karta. Hur användandet av höghastighetskameror på 60 Hz påverkar kvalitén av trackningen undersöktes. Inverkan visade sig vara mindre än förväntad. Tracking-stabiliteten ökade mycket i övergången från 10 Hz till 30 Hz video. Mätningarna visade dock att det knappt var någon skillnad att gå från 30 Hz till 60 Hz. I 60 Hz så blir skillnaden mellan bildrutor mindre med det gav inte bättre trackning. Anledningen till detta är med största sannolikhet att 30 Hz ger tillräckligt mjuka rörelser för att kunna tracka rörelser i de hastigheter som är aktuella för helikoptern. Den begränsande faktorn är därför att den algoritm som används inte klarar av alla typer av scener och det kommer inte kunna lösas med snabbare och bättre kameror utan kommer kräva förbättringar av SLAM algoritmen. Lokaliseringen fungerar bra när det finns många framträdande särdrag. Precisionen i de fallen har mätts till att ha ett RMS fel på 2,4 cm jämfört med data från motion capture som kan antas vara exakt.
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30

Schuster, Martin Johannes [Verfasser], Michael [Akademischer Betreuer] Beetz, Michael [Gutachter] Beetz, and Rudolph [Gutachter] Triebel. "Collaborative Localization and Mapping for Autonomous Planetary Exploration : Distributed Stereo Vision-Based 6D SLAM in GNSS-Denied Environments / Martin Johannes Schuster ; Gutachter: Michael Beetz, Rudolph Triebel ; Betreuer: Michael Beetz." Bremen : Staats- und Universitätsbibliothek Bremen, 2019. http://d-nb.info/119415686X/34.

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31

Alexandersson, Johan, and Olle Nordin. "Implementation of SLAM Algorithms in a Small-Scale Vehicle Using Model-Based Development." Thesis, Linköpings universitet, Datorteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148612.

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As autonomous driving is rapidly becoming the next major challenge in the auto- motive industry, the problem of Simultaneous Localization And Mapping (SLAM) has never been more relevant than it is today. This thesis presents the idea of examining SLAM algorithms by implementing such an algorithm on a radio con- trolled car which has been fitted with sensors and microcontrollers. The software architecture of this small-scale vehicle is based on the Robot Operating System (ROS), an open-source framework designed to be used in robotic applications. This thesis covers Extended Kalman Filter (EKF)-based SLAM, FastSLAM, and GraphSLAM, examining these algorithms in both theoretical investigations, simulations, and real-world experiments. The method used in this thesis is model- based development, meaning that a model of the vehicle is first implemented in order to be able to perform simulations using each algorithm. A decision of which algorithm to be implemented on the physical vehicle is then made backed up by these simulation results, as well as a theoretical investigation of each algorithm. This thesis has resulted in a dynamic model of a small-scale vehicle which can be used for simulation of any ROS-compliant SLAM-algorithm, and this model has been simulated extensively in order to provide empirical evidence to define which SLAM algorithm is most suitable for this application. Out of the algo- rithms examined, FastSLAM was proven to the best candidate, and was in the final stage, through usage of the ROS package gMapping, successfully imple- mented on the small-scale vehicle.
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32

McVicker, Michael Charles. "Effects of different camera motions on the error in estimates of epipolar geometry between two dimensional images in order to provide a framework for solutions to vision based Simultaneous Localization and Mapping (SLAM)." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Sep%5FMcVicker.pdf.

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Thesis (M.S. in Computer Science)--Naval Postgraduate School, September 2007.
Thesis Advisor(s): Kölsch, Mathias ; Squire, Kevin. "September 2007." Description based on title screen as viewed on October 24, 2007. Includes bibliographical references (p. 169-171). Also available in print.
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33

Mahdoui, Chedly Nesrine. "Communicating multi-UAV system for cooperative SLAM-based exploration." Thesis, Compiègne, 2018. http://www.theses.fr/2018COMP2447/document.

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Dans la communauté robotique aérienne, un croissant intérêt pour les systèmes multirobot (SMR) est apparu ces dernières années. Cela a été motivé par i) les progrès technologiques, tels que de meilleures capacités de traitement à bord des robots et des performances de communication plus élevées, et ii) les résultats prometteurs du déploiement de SMR tels que l’augmentation de la zone de couverture en un minimum de temps. Le développement d’une flotte de véhicules aériens sans pilote (UAV: Unmanned Aerial Vehicle) et de véhicules aériens de petite taille (MAV: Micro Aerial Vehicle) a ouvert la voie à de nouvelles applications à grande échelle nécessitant les caractéristiques de tel système de systèmes dans des domaines tels que la sécurité, la surveillance des catastrophes et des inondations, la recherche et le sauvetage, l’inspection des infrastructures, et ainsi de suite. De telles applications nécessitent que les robots identifient leur environnement et se localisent. Ces tâches fondamentales peuvent être assurées par la mission d’exploration. Dans ce contexte, cette thèse aborde l’exploration coopérative d’un environnement inconnu en utilisant une équipe de drones avec vision intégrée. Nous avons proposé un système multi-robot où le but est de choisir des régions spécifiques de l’environnement à explorer et à cartographier simultanément par chaque robot de manière optimisée, afin de réduire le temps d’exploration et, par conséquent, la consommation d’énergie. Chaque UAV est capable d’effectuer une localisation et une cartographie simultanées (SLAM: Simultaneous Localization And Mapping) à l’aide d’un capteur visuel comme principale modalité de perception. Pour explorer les régions inconnues, les cibles – choisies parmi les points frontières situés entre les zones libres et les zones inconnues – sont assignées aux robots en considérant un compromis entre l’exploration rapide et l’obtention d’une carte détaillée. À des fins de prise de décision, les UAVs échangent habituellement une copie de leur carte locale, mais la nouveauté dans ce travail est d’échanger les points frontières de cette carte, ce qui permet d’économiser la bande passante de communication. L’un des points les plus difficiles du SMR est la communication inter-robot. Nous étudions cette partie sous les aspects topologiques et typologiques. Nous proposons également des stratégies pour faire face à l’abandon ou à l’échec de la communication. Des validations basées sur des simulations étendues et des bancs d’essai sont présentées
In the aerial robotic community, a growing interest for Multi-Robot Systems (MRS) appeared in the last years. This is thanks to i) the technological advances, such as better onboard processing capabilities and higher communication performances, and ii) the promising results of MRS deployment, such as increased area coverage in minimum time. The development of highly efficient and affordable fleet of Unmanned Aerial Vehicles (UAVs) and Micro Aerial Vehicles (MAVs) of small size has paved the way to new large-scale applications, that demand such System of Systems (SoS) features in areas like security, disaster surveillance, inundation monitoring, search and rescue, infrastructure inspection, and so on. Such applications require the robots to identify their environment and localize themselves. These fundamental tasks can be ensured by the exploration mission. In this context, this thesis addresses the cooperative exploration of an unknown environment sensed by a team of UAVs with embedded vision. We propose a multi-robot framework where the key problem is to cooperatively choose specific regions of the environment to be simultaneously explored and mapped by each robot in an optimized manner in order to reduce exploration time and, consequently, energy consumption. Each UAV is able to performSimultaneous Localization And Mapping (SLAM) with a visual sensor as the main input sensor. To explore the unknown regions, the targets – selected from the computed frontier points lying between free and unknown areas – are assigned to robots by considering a trade-off between fast exploration and getting detailed grid maps. For the sake of decision making, UAVs usually exchange a copy of their local map; however, the novelty in this work is to exchange map frontier points instead, which allow to save communication bandwidth. One of the most challenging points in MRS is the inter-robot communication. We study this part in both topological and typological aspects. We also propose some strategies to cope with communication drop-out or failure. Validations based on extensive simulations and testbeds are presented
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34

Fujimoto, Masaki Stanley. "Graph-Based Whole Genome Phylogenomics." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8461.

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Understanding others is a deeply human urge basic in our existential quest. It requires knowing where someone has come from and where they sit amongst peers. Phylogenetic analysis and genome wide association studies seek to tell us where we’ve come from and where we are relative to one another through evolutionary history and genetic makeup. Current methods do not address the computational complexity caused by new forms of genomic data, namely long-read DNA sequencing and increased abundances of assembled genomes, that are becoming evermore abundant. To address this, we explore specialized data structures for storing and comparing genomic information. This work resulted in the creation of novel data structures for storing multiple genomes that can be used for identifying structural variations and other types of polymorphisms. Using these methods we illuminate the genetic history of organisms in our efforts to understand the world around us.
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Dixit-Radiya, Vibha. "Mapping on wormhole-routed distributed-memory systems : a temporal communication graph-based approach /." The Ohio State University, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487863429091928.

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36

Ellingson, Gary James. "Cooperative Navigation of Fixed-Wing Micro Air Vehicles in GPS-Denied Environments." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8706.

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Micro air vehicles have recently gained popularity due to their potential as autonomous systems. Their future impact, however, will depend in part on how well they can navigate in GPS-denied and GPS-degraded environments. In response to this need, this dissertation investigates a potential solution for GPS-denied operations called relative navigation. The method utilizes keyframe-to-keyframe odometry estimates and their covariances in a global back end that represents the global state as a pose graph. The back end is able to effectively represent nonlinear uncertainties and incorporate opportunistic global constraints. The GPS-denied research community has, for the most part, neglected to consider fixed-wing aircraft. This dissertation enables fixed-wing aircraft to utilize relative navigation by accounting for their sensing requirements. The development of an odometry-like, front-end, EKF-based estimator that utilizes only a monocular camera and an inertial measurement unit is presented. The filter uses the measurement model of the multi-state-constraint Kalman filter and regularly performs relative resets in coordination with keyframe declarations. In addition to the front-end development, a method is provided to account for front-end velocity bias in the back-end optimization. Finally a method is presented for enabling multiple vehicles to improve navigational accuracy by cooperatively sharing information. Modifications to the relative navigation architecture are presented that enable decentralized, cooperative operations amidst temporary communication dropouts. The proposed framework also includes the ability to incorporate inter-vehicle measurements and utilizes a new concept called the coordinated reset, which is necessary for optimizing the cooperative odometry and improving localization. Each contribution is demonstrated through simulation and/or hardware flight testing. Simulation and Monte-Carlo testing is used to show the expected quality of the results. Hardware flight-test results show the front-end estimator performance, several back-end optimization examples, and cooperative GPS-denied operations.
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37

Relfsson, Emil. "Map Partition and Loop Closure in a Factor Graph Based SAM System." Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171894.

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The graph-based formulation of the navigation problem is establishing itself as one of the standard ways to formulate the navigation problem within the sensor fusion community. It enables a convenient way to access information from previous positions which can be used to enhance the estimate of the current position.To restrict working memory usage, map partitioning can be used to store older parts of the map on a hard drive, in the form of submaps. This limits the number of previous positions within the active map. This thesis examines the effect that map partitioning information loss has on the state of the art positioning algorithm iSAM2, both in open routes and when loop closure is achieved. It finds that larger submaps appear to cause a smaller positional error than smaller submaps for open routes. The smaller submaps seem to give smaller positional error than larger submaps when loop closure is achieved. The thesis also examines how the density of landmarks at the partition point affects the positional error, but the obtained result is mixed and no clear conclusions can be made. Finally it reviews some loop closure detection algorithms that can be convenient to pair with the iSAM2 algorithm.
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38

Matos, Jody Maick Araujo de. "Graph based algorithms to efficiently map VLSI circuits with simple cells." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/174523.

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Essa tese introduz um conjunto de algoritmos baseados em grafos para o mapeamento eficiente de circuitos VLSI com células simples. Os algoritmos propostos se baseiam em minimizar de maneira eficiente o número de elementos lógicos usados na implementação do circuito. Posteriormente, uma quantidade significativa de esforço é aplicada na minimização do número de inversores entre esses elementos lógicos. Por fim, essa representação lógica é mapeada para circuitos compostos somente por células NAND e NOR de duas entradas, juntamente com inversores. Células XOR e XNOR de duas entradas também podem ser consideradas. Como nós também consideramos circuitos sequenciais, flips-flops também são levados em consideração. Com o grande esforço de minimização de elementos lógicos, o circuito gerado pode conter algumas células com um fanout impraticável para os nodos tecnológicos atuais. Para corrigir essas ocorrências, nós propomos um algoritmo de limitação de fanout que considera tanto a área sendo utilizada pelas células quanto a sua profundidade lógica. Os algoritmos propostos foram aplicados sobre um conjunto de circuitos de benchmark e os resultados obtidos demonstram a utilidade dos métodos. Essa tese introduz um conjunto de algoritmos baseados em grafos para o mapeamento eficiente de circuitos VLSI com células simples. Os algoritmos propostos se baseiam em minimizar de maneira eficiente o número de elementos lógicos usados na implementação do circuito. Posteriormente, uma quantidade significativa de esforço é aplicada na minimização do número de inversores entre esses elementos lógicos. Por fim, essa representação lógica é mapeada para circuitos compostos somente por células NAND e NOR de duas entradas, juntamente com inversores. Células XOR e XNOR de duas entradas também podem ser consideradas. Como nós também consideramos circuitos sequenciais, flips-flops também são levados em consideração. Com o grande esforço de minimização de elementos lógicos, o circuito gerado pode conter algumas células com um fanout impraticável para os nodos tecnológicos atuais. Para corrigir essas ocorrências, nós propomos um algoritmo de limitação de fanout que considera tanto a área sendo utilizada pelas células quanto a sua profundidade lógica. Os algoritmos propostos foram aplicados sobre um conjunto de circuitos de benchmark e os resultados obtidos demonstram a utilidade dos métodos. Adicionalmente, algumas aplicações Morethan-Moore, tais como circuitos baseados em eletrônica impressa, também podem ser beneficiadas pela abordagem proposta.
This thesis introduces a set of graph-based algorithms for efficiently mapping VLSI circuits using simple cells. The proposed algorithms are concerned to, first, effectively minimize the number of logic elements implementing the synthesized circuit. Then, we focus a significant effort on minimizing the number of inverters in between these logic elements. Finally, this logic representation is mapped into a circuit comprised of only two-input NANDs and NORS, along with the inverters. Two-input XORs and XNORs can also be optionally considered. As we also consider sequential circuits in this work, flip-flops are taken into account as well. Additionally, with high-effort optimization on the number of logic elements, the generated circuits may contain some cells with unfeasible fanout for current technology nodes. In order to fix these occurrences, we propose an area-oriented, level-aware algorithm for fanout limitation. The proposed algorithms were applied over a set of benchmark circuits and the obtained results have shown the usefulness of the method. We show that efficient implementations in terms of inverter count, transistor count, area, power and delay can be generated from circuits with a reduced number of both simple cells and inverters, combined with XOR/XNOR-based optimizations. The proposed buffering algorithm can handle all unfeasible fanout occurrences, while (i) optimizing the number of added inverters; and (ii) assigning cells to the inverter tree based on their level criticality. When comparing with academic and commercial approaches, we are able to simultaneously reduce the average number of inverters, transistors, area, power dissipation and delay up to 48%, 5%, 5%, 5%, and 53%, respectively. As the adoption of a limited set of simple standard cells have been showing benefits for a variety of modern VLSI circuits constraints, such as layout regularity, routability constraints, and/or ultra low power constraints, the proposed methods can be of special interest for these applications. Additionally, some More-than-Moore applications, such as printed electronics designs, can also take benefit from the proposed approach.
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Matos, Jody Maick Araujo de. "Graph-based algorithms for transistor count minimization in VLSI circuit EDA tools." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/147759.

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Esta dissertação de mestrado introduz um conjunto de algoritmos baseados em grafos para a obtenção de circuitos VLSI com um número reduzido de transistores utilziando células simples. Esses algoritmos têm um foco principal na minimização do número de nodos em representações AIG e mapear essa estrutura otimizada utilizando células simples (NAND2 e NOR2) com um número mínimo de inversores. Devido à minimização de nodos, o AIG tem um alto compartilhamento lógico, o que pode derivar circuitos intermediários contendo células com fanouts infactíveis para os nodos tecnológicos atuais. De forma a resolver essas ocorrências, o circuito intermediário é submetido a um algoritmo para limitação de fanout. Os algoritmos propostos foram aplicados num conjunto de circuitos de benchmark e os resultados obtidos mostram a utilidade do método. Os circuitos resultantes tiveram, em média, 32% menos transistores do que as referências anteriores em números de transistores utilizando células simples. Adicionalmente, quando comparando esses resultados com trabalhos que utilizam células complexas, nossos números demonstraram que abordagens anteriores estão algumas vezes longe do número mínimo de transistores que pode ser obtido com o uso eficiente de uma biblioteca reduzida de células, composta por poucas células simples. Os circuitos baseados em células simples obtidos com a aplicação dos algoritmos proposto neste trabalho apresentam um menor número de transistores em muitos casos quando comparados aos resultados previamente publicados utilizando células complexas (CMOS estático e PTL).
This master’s thesis introduces a set of graph-based algorithms for obtaining reduced transistor count VLSI circuits using simple cells. These algorithms are mainly focused on minimizing node count in AIG representations and mapping this optimized AIG using simple cells (NAND2 and NOR2) with a minimal number of inverters. Due to the AIG node count minimization, the logic sharing is probably highly present in the optimized AIG, what may derive intermediate circuits containing cells with unfeasible fanout in current technology nodes. In order to fix these occurrences, this intermediate circuit is subjected to an algorithm for fanout limitation. The proposed algorithms were applied over a set of benchmark circuits and the obtained results have shown the usefulness of the method. The circuits generated by the methods proposed herein have, in average, 32% less transistor than the previous reference on transistor count using simple cells. Additionally, when comparing the presented results in terms of transistor count against works advocating for complex cells, our results have demonstrated that previous approaches are sometimes far from the minimum transistor count that can be obtained with the efficient use of a reduced cell library composed by only a few number of simple cells. The simple-cells-based circuits obtained after applying the algorithms proposed herein have presented a lower transistor count in many cases when compared to previously published results using complex (static CMOS and PTL) cells.
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40

Wheeler, David Orton. "Relative Navigation of Micro Air Vehicles in GPS-Degraded Environments." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6609.

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Most micro air vehicles rely heavily on reliable GPS measurements for proper estimation and control, and therefore struggle in GPS-degraded environments. When GPS is not available, the global position and heading of the vehicle is unobservable. This dissertation establishes the theoretical and practical advantages of a relative navigation framework for MAV navigation in GPS-degraded environments. This dissertation explores how the consistency, accuracy, and stability of current navigation approaches degrade during prolonged GPS dropout and in the presence of heading uncertainty. Relative navigation (RN) is presented as an alternative approach that maintains observability by working with respect to a local coordinate frame. RN is compared with several current estimation approaches in a simulation environment and in hardware experiments. While still subject to global drift, RN is shown to produce consistent state estimates and stable control. Estimating relative states requires unique modifications to current estimation approaches. This dissertation further provides a tutorial exposition of the relative multiplicative extended Kalman filter, presenting how to properly ensure observable state estimation while maintaining consistency. The filter is derived using both inertial and body-fixed state definitions and dynamics. Finally, this dissertation presents a series of prolonged flight tests, demonstrating the effectiveness of the relative navigation approach for autonomous GPS-degraded MAV navigation in varied, unknown environments. The system is shown to utilize a variety of vision sensors, work indoors and outdoors, run in real-time with onboard processing, and not require special tuning for particular sensors or environments. Despite leveraging off-the-shelf sensors and algorithms, the flight tests demonstrate stable front-end performance with low drift. The flight tests also demonstrate the onboard generation of a globally consistent, metric, and localized map by identifying and incorporating loop-closure constraints and intermittent GPS measurements. With this map, mission objectives are shown to be autonomously completed.
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41

Naser, Taher Ahmed Jabir. "A flexible approach for mapping between object-oriented databases and XML : a two way method based on an object graph." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5505.

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One of the most popular challenges facing academia and industry is the development of effective techniques and tools for maximizing the availability of data as the most valuable source of knowledge. The internet has dominated as the core for maximizing data availability and XML (eXtensible Markup Language) has emerged and is being gradually accepted as the universal standard format for platform independent publishing and exchanging data over the Internet. On the other hand, there remain large amount of data held in structured databases and database management systems have been traditionally used for the effective storage and manipulation of large volumes of data. This raised the need for effective methodologies capable of smoothly transforming data between different formats in general and between XML and structured databases in particular. This dissertation addresses the issue by proposing a two-way mapping approach between XML and object-oriented databases. The basic steps of the proposed approach are applied in a systematic way to produce a graph from the source and then transform the graph into the destination format. In other words, the derived graph summarizes characteristics of the source whether XML (elements and attributes) or object-oriented database (classes, inheritance and nesting hierarchies). Then, the developed methodology classifies nodes and links from the graph into the basic constructs of the destination, i.e., elements and attributes for XML or classes, inheritance and nesting hierarchies for object-oriented databases. The methodology has been successfully implemented and illustrative case studies are presented in this document.
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Naser, Taher A. J. "A flexible approach for mapping between object-oriented databases and xml. A two way method based on an object graph." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5505.

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One of the most popular challenges facing academia and industry is the development of effective techniques and tools for maximizing the availability of data as the most valuable source of knowledge. The internet has dominated as the core for maximizing data availability and XML (eXtensible Markup Language) has emerged and is being gradually accepted as the universal standard format for platform independent publishing and exchanging data over the Internet. On the other hand, there remain large amount of data held in structured databases and database management systems have been traditionally used for the effective storage and manipulation of large volumes of data. This raised the need for effective methodologies capable of smoothly transforming data between different formats in general and between XML and structured databases in particular. This dissertation addresses the issue by proposing a two-way mapping approach between XML and object-oriented databases. The basic steps of the proposed approach are applied in a systematic way to produce a graph from the source and then transform the graph into the destination format. In other words, the derived graph summarizes characteristics of the source whether XML (elements and attributes) or object-oriented database (classes, inheritance and nesting hierarchies). Then, the developed methodology classifies nodes and links from the graph into the basic constructs of the destination, i.e., elements and attributes for XML or classes, inheritance and nesting hierarchies for object-oriented databases. The methodology has been successfully implemented and illustrative case studies are presented in this document.
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43

Jackson, James Scott. "Enabling Autonomous Operation of Micro Aerial Vehicles Through GPS to GPS-Denied Transitions." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8709.

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Micro aerial vehicles and other autonomous systems have the potential to truly transform life as we know it, however much of the potential of autonomous systems remains unrealized because reliable navigation is still an unsolved problem with significant challenges. This dissertation presents solutions to many aspects of autonomous navigation. First, it presents ROSflight, a software and hardware architure that allows for rapid prototyping and experimentation of autonomy algorithms on MAVs with lightweight, efficient flight control. Next, this dissertation presents improvments to the state-of-the-art in optimal control of quadrotors by utilizing the error-state formulation frequently utilized in state estimation. It is shown that performing optimal control directly over the error-state results in a vastly more computationally efficient system than competing methods while also dealing with the non-vector rotation components of the state in a principled way. In addition, real-time robust flight planning is considered with a method to navigate cluttered, potentially unknown scenarios with real-time obstacle avoidance. Robust state estimation is a critical component to reliable operation, and this dissertation focuses on improving the robustness of visual-inertial state estimation in a filtering framework by extending the state-of-the-art to include better modeling and sensor fusion. Further, this dissertation takes concepts from the visual-inertial estimation community and applies it to tightly-coupled GNSS, visual-inertial state estimation. This method is shown to demonstrate significantly more reliable state estimation than visual-inertial or GNSS-inertial state estimation alone in a hardware experiment through a GNSS-GNSS denied transition flying under a building and back out into open sky. Finally, this dissertation explores a novel method to combine measurements from multiple agents into a coherent map. Traditional approaches to this problem attempt to solve for the position of multiple agents at specific times in their trajectories. This dissertation instead attempts to solve this problem in a relative context, resulting in a much more robust approach that is able to handle much greater intial error than traditional approaches.
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44

Wan, Wei. "A New Approach to the Decomposition of Incompletely Specified Functions Based on Graph Coloring and Local Transformation and Its Application to FPGA Mapping." PDXScholar, 1992. https://pdxscholar.library.pdx.edu/open_access_etds/4698.

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The thesis presents a new approach to the decomposition of incompletely specified functions and its application to FPGA (Field Programmable Gate Array) mapping. Five methods: Variable Partitioning, Graph Coloring, Bond Set Encoding, CLB Reusing and Local Transformation are developed in order to efficiently perform decomposition and FPGA (Lookup-Table based FPGA) mapping. 1) Variable Partitioning is a high quality hemistic method used to find the "best" partitions, avoiding the very time consuming testing of all possible decomposition charts, which is impractical when there are many input variables in the input function. 2) Graph Coloring is another high quality heuristic\ used to perform the quasi-optimum don't care assignment, making the program possible to accept incompletely specified function and perform a quasi-optimum assignment to the unspecified part of the function. 3) Bond Set Encoding algorithm is used to simplify the decomposed blocks during the process of decomposition. 4) CLB Reusing algorithm is used to reduce the number of CLBs used in the final mapped circuit. 5) Local Transformation concept is introduced to transform nondecomposable functions into decomposable ones, thus making it possible to apply decomposition method to FPGA mapping. All the above developed methods are incorporated into a program named TRADE, which performs global optimization over the input functions. While most of the existing methods recursively perform local optimization over some kinds of network-like graphs, and few of them can handle incompletely specified functions. Cube calculus is used in the TRADE program, the operations are global and very fast. A short description of the TRADE program and the evaluation of the results are provided at the_ end of the thesis. For many benchmarks the TRADE program gives better results than any program published in the literature.
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Sharma, Rajnikant. "Bearing-Only Cooperative-Localization and Path-Planning of Ground and Aerial Robots." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2884.

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In this dissertation, we focus on two fundamental problems related to the navigation of ground robots and small Unmanned Aerial Vehicle (UAVs): cooperative localization and path planning. The theme running through in all of the work is the use of bearing only sensors, with a focus on monocular video cameras mounted on ground robots and UAVs. To begin with, we derive the conditions for the complete observability of the bearing-only cooperative localization problem. The key element of this analysis is the Relative Position Measurement Graph (RPMG). The nodes of an RPMG represent vehicle states and the edges represent bearing measurements between nodes. We show that graph theoretic properties like the connectivity and the existence of a path between two nodes can be used to explain the observability of the system. We obtain the maximum rank of the observability matrix without global information and derive conditions under which the maximum rank can be achieved. Furthermore, we show that for the complete observability, all of the nodes in the graph must have a path to at least two different landmarks of known location. The complete observability can also be obtained without landmarks if the RPMG is connected and at least one of the robots has a sensor which can measure its global pose, for example a GPS receiver. We validate these conditions by simulation and experimental results. The theoretical conditions to attain complete observability in a localization system is an important step towards reliable and efficient design of localization and path planning algorithms. With such conditions, a designer does not need to resort to exhaustive simulations and/or experimentation to verify whether a given selection of a control strategy, topology of the sensor network, and sensor measurements meets the observability requirements of the system. In turn, this leads to decreased requirements of time, cost, and effort for designing a localization algorithms. We use these observability conditions to develop a technique, for camera equipped UAVs, to cooperatively geo-localize a ground target in an urban terrain. We show that the bearing-only cooperative geo-localization technique overcomes the limitation of requiring a low-flying UAV to maintain line-of-sight while flying high enough to maintain GPS lock. We design a distributed path planning algorithm using receding horizon control that improves the localization accuracy of the target and of all of the UAVs while satisfying the observability conditions. Next, we use the observability analysis to explicitly design an active local path planning algorithm for UAVs. The algorithm minimizes the uncertainties in the time-to-collision (TTC) and bearing estimates while simultaneously avoiding obstacles. Using observability analysis we show that maximizing the observability and collision avoidance are complementary tasks. We provide sufficient conditions of the environment which maximizes the chances obstacle avoidance and UAV reaching the goal. Finally, we develop a reactive path planner for UAVs using sliding mode control such that it does not require range from the obstacle, and uses bearing to obstacle to avoid cylindrical obstacles and follow straight and curved walls. The reactive guidance strategy is fast, computationally inexpensive, and guarantees collision avoidance.
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46

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

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Les travaux réalisés au cours de cette thèse s'inscrivent dans la problématique de localisation en temps réel d'une caméra par vision monoculaire. Dans la littérature, il existe différentes méthodes qui peuvent être classées en trois catégories. La première catégorie de méthodes considère une caméra évoluant dans un environnement complètement inconnu (SLAM). Cette méthode réalise une reconstruction enligne de primitives observées dans des images d'une séquence vidéo et utilise cette reconstruction pour localiser la caméra. Les deux autres permettent une localisation par rapport à un objet 3D de la scène en s'appuyant sur la connaissance, a priori, d'un modèle de cet objet (suivi basé modèle). L'une utilise uniquement l'information du modèle 3D de l'objet pour localiser la caméra, l'autre peut être considérée comme l'intermédiaire entre le SLAM et le suivi basé modèle. Cette dernière méthode consiste à localiser une caméra par rapport à un objet en utilisant, d'une part, le modèle de ce dernier et d'autre part, une reconstruction en ligne des primitives de l'objet d'intérêt. Cette reconstruction peut être assimilée à une mise à jour du modèle initial (suivi basé modèle avec mise à jour). Chacune de ces méthodes possède des avantages et des inconvénients. Dans le cadre de ces travaux de thèse, nous proposons une solution unifiant l'ensemble de ces méthodes de localisation dans un unique cadre désigné sous le terme de SLAM contraint. Cette solution, qui unifie ces différentes méthodes, permet de tirer profit de leurs avantages tout en limitant leurs inconvénients respectifs. En particulier, nous considérons que la caméra évolue dans un environnement partiellement connu, c'est-à-dire pour lequel un modèle (géométrique ou photométrique) 3D d'un objet statique de la scène est disponible. L'objectif est alors d'estimer de manière précise la pose de la caméra par rapport à cet objet 3D. L'information absolue issue du modèle 3D de l'objet d'intérêt est utilisée pour améliorer la localisation de type SLAM en incluant cette information additionnelle directement dans le processus d'ajustement de faisceaux. Afin de pouvoir gérer un large panel d'objets 3D et de scènes, plusieurs types de contraintes sont proposées dans ce mémoire. Ces différentes contraintes sont regroupées en deux approches. La première permet d'unifier les méthodes SLAM et de suivi basé modèle, en contraignant le déplacement de la caméra via la projection de primitives existantes extraites du modèle 3D dans les images. La seconde unifie les méthodes SLAM et de suivi basé modèle avec mise à jour en contraignant les primitives reconstruites par le SLAM à appartenir à la surface du modèle (unification SLAM et mise à jour du modèle). Les avantages de ces différents ajustements de faisceaux contraints, en terme de précision, de stabilité de recalage et de robustesse aux occultations, sont démontrés sur un grand nombre de données de synthèse et de données réelles. Des applications temps réel de réalité augmentée sont également présentées sur différents types d'objets 3D. Ces travaux ont fait l'objet de 4 publications internationales, de 2 publications nationales et d'un dépôt de brevet.
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47

Matula, Radek. "Grafická reprezentace grafů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236753.

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This Master Thesis deals with the drawing algorithms of graphs known from the mathematical theory. These algorithms deals with an appropriate distribution of the graph vertices in order to obtain the most clear and readable graphs for human readers. The main objective of this work was also to implement the drawing algorithm in the application that would allow to edit the graph. This work deals also with graphs representation in computers.
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48

Fekih, Hassen Wiem. "A ubiquitous navigation service on smartphones." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI006.

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La navigation pédestre est un domaine de recherche en pleine croissance qui vise à développer des services assurant le positionnement et la navigation en continu des personnes à l'extérieur comme à l'intérieur de bâtiments. Dans cette thèse, nous proposons un prototype de service pour la navigation pédestre ubiquitaire qui tient compte des préférences de l'utilisateur et de la technologie de positionnement optimale disponible. Notre objectif principal est d'estimer, d'une façon continue, la position d'un piéton muni d'un smartphone. En premier lieu, nous proposons un nouvel algorithme, nommé UCOSA, qui permet de sélectionner la technologie de positionnement à adopter à tout moment le long du processus de navigation. L'algorithme UCOSA commence par inférer la nécessité de déclencher un processus de "handover" (changement de technologie) entre les technologies de positionnement détectées (i.e. quand les zones de couvertures se chevauchent) en utilisant la technique de la logique floue. Ensuite, il sélectionne la technologie optimale à l'aide d'une fonction qui calcule un score pour chaque technologie disponible et qui se compose de deux parties. La première partie représente les poids, calculés en utilisant la méthode d'analyse hiérarchique (AHP). Tandis que, la deuxième partie fournit les valeurs normalisées des paramètres considérés. L'algorithme UCOSA intègre aussi la technique de positionnement à l'estime appelé PDR afin d'améliorer le calcul de la position du smartphone. En second lieu, nous portons l'intérêt à la technique de positionnement par empreintes RSS dont le principe consiste à calculer la position du smartphone en comparant les valeurs RSSs enregistrées, en temps réel, avec les valeurs RSSs stockées dans une base de données (radiomap). La majorité des radiomaps sont représentées sous forme de grilles composées de points de référence (PR). Nous proposons une nouvelle conception de radiomap qui ajoute d'autres PRs au centre de gravité de chaque carré de la grille. En troisième lieu, nous abordons le problème de la construction du graphe modélisant un bâtiment multi-étages. Nous proposons un algorithme qui crée tout d'abord un graphe plan pour chaque étage, séparément, et qui relie ensuite les différents étages par des liens verticaux. En dernier lieu, nous étudions un nouvel algorithme nommé SIONA qui calcule et qui affiche d'une manière continue le chemin entre deux points situés à l'intérieur ou à l'extérieur d'un bâtiment. Plusieurs expériences réelles ont été réalisées pour évaluer les performances des algorithmes proposés avec des résultats prometteurs en termes de continuité et de précision (de l'ordre de 1.8 m) du service de navigation
Pedestrian navigation is a growing research field, which aims at developing services and applications that ensure the continuous positioning and navigation of people inside and outside covered areas (e.g. buildings). In this thesis, we propose a ubiquitous pedestrian navigation service based on user preferences and the most suitable efficient available positioning technology (e.g. WiFi, GNSS). Our main objective is to estimate continuously the position of a pedestrian carrying a smartphone equipped with a variety of technologies and sensors. First, we propose a novel positioning technology selection algorithm, called UCOSA for the complete ubiquitous navigation service in indoor and outdoor environments. UCOSA algorithm starts by inferring the need of a handover between the available positioning technologies on the overlapped coverage areas using fuzzy logic technique. If a handover process is required, a score is calculated for each captured Radio Frequency (RF) positioning technology. The score function consists of two parts: the first part represents the user preferences weights computed based on the Analytic Hierarchy Process (AHP). Whereas, the second part provides the user requirements (normalized values). UCOSA algorithm also integrates the Pedestrian Dead Reckoning (PDR) positioning technique through the navigation process to enhance the estimation of the smartphone's position. Second, we focus on the RSS fingerprinting positioning technique as it is the most widely used technique, which principle is to return the smartphone's position by comparing the real time recorded RSS values with the radiomap (i.e. a database of previous stored RSS values). Most of radiomap are organized in a grid, formed or Reference Point (RP): we propose a new design of radiomap which complements the grid with other RPs located at the center of gravity of each grid square. Third, we address the challenge of constructing a graph for a multi-floor building. We propose an algorithm that starts by creating the horizontal graph of each floor, separately, and then, adds vertical links between the different floors. Finally, we implement a novel algorithm, called SIONA that calculates and displays in a continuous manner the pathway between two distinct points being located indoor or outdoor. We conduct several real experiments inside the campus of the University of Passau in Germany to evaluate the performance of the proposed algorithms. They yield promising results in terms of continuity and accuracy (around 1.8 m indoor) of navigation service
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49

Chung, Lung-Yang, and 鍾隆揚. "Deep Learning Based Indoor Localization and Mapping." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/388s7a.

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碩士
國立中興大學
電機工程學系所
107
Nowadays everyone has a smart phone at least. We can use it with services provided by Google if we want location and map. However, GPS signal can not be found when we stay indoors. We can not use GoogleMap in this situation. Deep learning have a great success in the computer vision field(for example, image classification, object detection). This thesis purposes a method using deep learning to solve the indoor localization and mapping problem. We split it into two sub-tasks, and solve individually with two deep learning models. To evaluate our models, we experiments with different datasets. Evaluating models on the real world dataset, we obtain the average error of localization model is 0.59m and mapping one is 0.65m.
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50

Milstein, Adam. "Improved Particle Filter Based Localization and Mapping Techniques." Thesis, 2008. http://hdl.handle.net/10012/3619.

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One of the most fundamental problems in mobile robotics is localization. The solution to most problems requires that the robot first determine its location in the environment. Even if the absolute position is not necessary, the robot must know where it is in relation to other objects. Virtually all activities require this preliminary knowledge. Another part of the localization problem is mapping, the robot’s position depends on its representation of the environment. An object’s position cannot be known in isolation, but must be determined in relation to the other objects. A map gives the robot’s understanding of the world around it, allowing localization to provide a position within that representation. The quality of localization thus depends directly on the quality of mapping. When a robot is moving in an unknown environment these problems must be solved simultaneously in a problem called SLAM (Simultaneous Localization and Mapping). Some of the best current techniques for localization and SLAM are based on particle filters which approximate the belief state. Monte Carlo Localization (MCL) is a solution to basic localization, while FastSLAM is used to solve the SLAM problem. Although these techniques are powerful, certain assumptions reduce their effectiveness. In particular, both techniques assume an underlying static environment, as well as certain basic sensor models. Also, MCL applies to the case where the map is entirely known while FastSLAM solves an entirely unknown map. In the case of partial knowledge, MCL cannot succeed while FastSLAM must discard the additional information. My research provides improvements to particle based localization and mapping which overcome some of the problems with these techniques, without reducing the original capabilities of the algorithms. I also extend their application to additional situations and make them more robust to several types of error. The improved solutions allow more accurate localization to be performed, so that robots can be used in additional situations.
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