23 research outputs found

    Contributions to Localization, Mapping and Navigation in Mobile Robotics

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    This thesis focuses on the problem of enabling mobile robots to autonomously build world models of their environments and to employ them as a reference to self–localization and navigation. For mobile robots to become truly autonomous and useful, they must be able of reliably moving towards the locations required by their tasks. This simple requirement gives raise to countless problems that have populated research in the mobile robotics community for the last two decades. Among these issues, two of the most relevant are: (i) secure autonomous navigation, that is, moving to a target avoiding collisions and (ii) the employment of an adequate world model for robot self-referencing within the environment and also for locating places of interest. The present thesis introduces several contributions to both research fields. Among the contributions of this thesis we find a novel approach to extend SLAM to large-scale scenarios by means of a seamless integration of geometric and topological map building in a probabilistic framework that estimates the hybrid metric-topological (HMT) state space of the robot path. The proposed framework unifies the research areas of topological mapping, reasoning on topological maps and metric SLAM, providing also a natural integration of SLAM and the “robot awakening” problem. Other contributions of this thesis cover a wide variety of topics, such as optimal estimation in particle filters, a new probabilistic observation model for laser scanners based on consensus theory, a novel measure of the uncertainty in grid mapping, an efficient method for range-only SLAM, a grounded method for partitioning large maps into submaps, a multi-hypotheses approach to grid map matching, and a mathematical framework for extending simple obstacle avoidance methods to realistic robots

    Stereo Visual SLAM for Mobile Robots Navigation

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    Esta tesis está enfocada a la combinación de los campos de la robótica móvil y la visión por computador, con el objetivo de desarrollar métodos que permitan a un robot móvil localizarse dentro de su entorno mientras construye un mapa del mismo, utilizando como única entrada un conjunto de imágenes. Este problema se denomina SLAM visual (por las siglas en inglés de "Simultaneous Localization And Mapping") y es un tema que aún continúa abierto a pesar del gran esfuerzo investigador realizado en los últimos años. En concreto, en esta tesis utilizamos cámaras estéreo para capturar, simultáneamente, dos imágenes desde posiciones ligeramente diferentes, proporcionando así información 3D de forma directa. De entre los problemas de localización de robots, en esta tesis abordamos dos de ellos: el seguimiento de robots y la localización y mapeado simultáneo (o SLAM). El primero de ellos no tiene en cuenta el mapa del entorno sino que calcula la trayectoria del robot mediante la composición incremental de las estimaciones de su movimiento entre instantes de tiempo consecutivos. Cuando se usan imágenes para calcular esta trayectoria, el problema toma el nombre de "odometría visual", y su resolución es más sencilla que la del SLAM visual. De hecho, a menudo se integra como parte de un sistema de SLAM completo. Esta tesis contribuye con la propuesta de dos sistemas de odometría visual. Uno de ellos está basado en un solución cerrada y eficiente mientras que el otro está basado en un proceso de optimización no-lineal que implementa un nuevo método de detección y eliminación rápida de espurios. Los métodos de SLAM, por su parte, también abordan la construcción de un mapa del entorno con el objetivo de mejorar sensiblemente la localización del robot, evitando de esta forma la acumulación de error en la que incurre la odometría visual. Además, el mapa construido puede ser empleado para hacer frente a situaciones exigentes como la recuperación de la localización tras la pérdida del robot o realizar localización global. En esta tesis se presentan dos sistemas completos de SLAM visual. Uno de ellos se ha implementado dentro del marco de los filtros probabilísticos no parámetricos, mientras que el otro está basado en un método nuevo de "bundle adjustment" relativo que ha sido integrado con algunas técnicas recientes de visión por computador. Otra contribución de esta tesis es la publicación de dos colecciones de datos que contienen imágenes estéreo capturadas en entornos urbanos sin modificar, así como una estimación del camino real del robot basada en GPS (denominada "ground truth"). Estas colecciones sirven como banco de pruebas para validar métodos de odometría y SLAM visual

    Adaptive Localization and Mapping for Planetary Rovers

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    Future rovers will be equipped with substantial onboard autonomy as space agencies and industry proceed with missions studies and technology development in preparation for the next planetary exploration missions. Simultaneous Localization and Mapping (SLAM) is a fundamental part of autonomous capabilities and has close connections to robot perception, planning and control. SLAM positively affects rover operations and mission success. The SLAM community has made great progress in the last decade by enabling real world solutions in terrestrial applications and is nowadays addressing important challenges in robust performance, scalability, high-level understanding, resources awareness and domain adaptation. In this thesis, an adaptive SLAM system is proposed in order to improve rover navigation performance and demand. This research presents a novel localization and mapping solution following a bottom-up approach. It starts with an Attitude and Heading Reference System (AHRS), continues with a 3D odometry dead reckoning solution and builds up to a full graph optimization scheme which uses visual odometry and takes into account rover traction performance, bringing scalability to modern SLAM solutions. A design procedure is presented in order to incorporate inertial sensors into the AHRS. The procedure follows three steps: error characterization, model derivation and filter design. A complete kinematics model of the rover locomotion subsystem is developed in order to improve the wheel odometry solution. Consequently, the parametric model predicts delta poses by solving a system of equations with weighed least squares. In addition, an odometry error model is learned using Gaussian processes (GPs) in order to predict non-systematic errors induced by poor traction of the rover with the terrain. The odometry error model complements the parametric solution by adding an estimation of the error. The gained information serves to adapt the localization and mapping solution to the current navigation demands (domain adaptation). The adaptivity strategy is designed to adjust the visual odometry computational load (active perception) and to influence the optimization back-end by including highly informative keyframes in the graph (adaptive information gain). Following this strategy, the solution is adapted to the navigation demands, providing an adaptive SLAM system driven by the navigation performance and conditions of the interaction with the terrain. The proposed methodology is experimentally verified on a representative planetary rover under realistic field test scenarios. This thesis introduces a modern SLAM system which adapts the estimated pose and map to the predicted error. The system maintains accuracy with fewer nodes, taking the best of both wheel and visual methods in a consistent graph-based smoothing approach

    A Drift-Resilient and Degeneracy-Aware Loop Closure Detection Method for Localization and Mapping In Perceptually-Degraded Environments

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    Enabling fully autonomous robots capable of navigating and exploring unknown and complex environments has been at the core of robotics research for several decades. Mobile robots rely on a model of the environment for functions like manipulation, collision avoidance and path planning. In GPS-denied and unknown environments where a prior map of the environment is not available, robots need to rely on the onboard sensing to obtain locally accurate maps to operate in their local environment. A global map of an unknown environment can be constructed from fusion of local maps of temporally or spatially distributed mobile robots in the environment. Loop closure detection, the ability to assert that a robot has returned to a previously visited location, is crucial for consistent mapping as it reduces the drift caused by error accumulation in the estimated robot trajectory. Moreover, in multi-robot systems, loop closure detection enables finding the correspondences between the local maps obtained by individual robots and merging them into a consistent global map of the environment. In ambiguous and perceptually-degraded environments, robust detection of intra- and inter-robot loop closures is especially challenging. This is due to poor illumination or lack-thereof, self-similarity, and sparsity of distinctive perceptual landmarks and features sufficient for establishing global position. Overcoming these challenges enables a wide range of terrestrial and planetary applications, ranging from search and rescue, and disaster relief in hostile environments, to robotic exploration of lunar and Martian surfaces, caves and lava tubes that are of particular interest as they can provide potential habitats for future manned space missions. In this dissertation, methods and metrics are developed for resolving location ambiguities to significantly improve loop closures in perceptually-degraded environments with sparse or undifferentiated features. The first contribution of this dissertation is development of a degeneracy-aware SLAM front-end capable of determining the level of geometric degeneracy in an unknown environment based on computing the Hessian associated with the computed optimal transformation from lidar scan matching. Using this crucial capability, featureless areas that could lead to data association ambiguity and spurious loop closures are determined and excluded from the search for loop closures. This significantly improves the quality and accuracy of localization and mapping, because the search space for loop closures can be expanded as needed to account for drift while decreasing rather than increasing the probability of false loop closure detections. The second contribution of this dissertation is development of a drift-resilient loop closure detection method that relies on the 2D semantic and 3D geometric features extracted from lidar point cloud data to enable detection of loop closures with increased robustness and accuracy as compared to traditional geometric methods. The proposed method achieves higher performance by exploiting the spatial configuration of the local scenes embedded in 2D occupancy grid maps commonly used in robot navigation, to search for putative loop closures in a pre-matching step before using a geometric verification. The third contribution of this dissertation is an extensive evaluation and analysis of performance and comparison with the state-of-the-art methods in simulation and in real-world, including six challenging underground mines across the United States

    Robust and efficient robotic mapping

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 123-129).Mobile robots are dependent upon a model of the environment for many of their basic functions. Locally accurate maps are critical to collision avoidance, while large-scale maps (accurate both metrically and topologically) are necessary for efficient route planning. Solutions to these problems have immediate and important applications to autonomous vehicles, precision surveying, and domestic robots. Building accurate maps can be cast as an optimization problem: find the map that is most probable given the set of observations of the environment. However, the problem rapidly becomes difficult when dealing with large maps or large numbers of observations. Sensor noise and non-linearities make the problem even more difficult especially when using inexpensive (and therefore preferable) sensors. This thesis describes an optimization algorithm that can rapidly estimate the maximum likelihood map given a set of observations. The algorithm, which iteratively reduces map error by considering a single observation at a time, scales well to large environments with many observations. The approach is particularly robust to noise and non-linearities, quickly escaping local minima that trap current methods. Both batch and online versions of the algorithm are described. In order to build a map, however, a robot must first be able to recognize places that it has previously seen. Limitations in sensor processing algorithms, coupled with environmental ambiguity, make this difficult. Incorrect place recognitions can rapidly lead to divergence of the map. This thesis describes a place recognition algorithm that can robustly handle ambiguous data. We evaluate these algorithms on a number of challenging datasets and provide quantitative comparisons to other state-of-the-art methods, illustrating the advantages of our methods.by Edwin B. Olson.Ph.D

    Mapping in urban environment for autonomous vehicle

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    Ph.DDOCTOR OF PHILOSOPH

    Aplicação de técnicas de fusão sensorial para mapeamento e localização simultâneos para robôs terrestres

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    Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.Um dos problemas que envolvem as soluções para a mobilidade de robôs móveis terrestres é estimar a posição do robô com precisão juntamente com a exploração do ambiente, mapeando-o corretamente (SLAM - Simultaneous Localization and Mapping - Localização e Mapeamento Simultâneo). Embora vários algoritmos tenham sido desenvolvidos nos últimos anos, exigindo uma carga de cálculo computacional cada vez maior dos robôs,, estes estão susceptíveis a um mau desempenho quando os sensores apresentam ruídos, quando há problemas nos atuadores, variáveis não modeladas ou em virtude de algum imprevisto momentâneo no ambiente. A proposta deste trabalho é programar um SLAM para robôs móveis interligando-o a uma combinação de sensores inerciais com sensores de odometria através de uma técnica de fusão de sensores conhecida como filtro de Kalman Estendido, para reduzir a incerteza na estimação da posição e melhorar o desempenho do SLAM. Por consequência, o custo computacional é reduzido. O trabalho foi estruturado iniciando por uma revisão a respeito dos conceitos básicos de sensoriamento, a fim de contextualizar o problema e apresentar as nomenclaturas e termos utilizados. A seguir foram abordadas as técnicas de fusão de dados, as representações do robô e do ambiente, as técnicas de mapeamento e exploração e as diversas técnicas de navegação que podem ser utilizadas, para ambientes conhecidos epara ambientes desconhecidos. Essas informações são importantes para um melhor entendimento do problema, de como representá-lo e de como se pode avaliar os resultados obtidos. Na sequência é apresentado o SLAM, destacando as principais técnicas e em detalhes o Grid Based FastSLAM. É demonstrado através de simulações que quanto maior as incertezas sobre a posição do robô, um número maior de partículas é necessário para manter a qualidade do mapa gerado, e como cada partícula possui um mapa associado a si, o custo computacional é consideravelmente aumentado. Outro aspecto analisado foi o impacto na escolha da covariância associada à transição de estados, propondo a utilização da covariância inerente ao cálculo da fusão de sensores como parâmetro de refinamento no SLAM.Abstract : One of the problems in solutions involving land mobile robots is the estimation of the robot position with precision and at the same time, explore the environment and mapping it correctly (SLAM - Simultaneous Localization and Mapping ). Several algorithms were developed in the last years, demanding large computational resources in robots and even so, these may have a bad performance in cases of sensors having noises, problems in actuators, not modeled variables or when there is something in the environment that wasn't expected. This dissertation proposal is to program a SLAM algorithm for mobile robots and connect it with a sensor data fusion, between odometry and inertial sensors, using the Extended Kalman Filter, achieving a reduction of the position uncertainty and improving the SLAM performance, also reducing the need of computational resources. This work begins with a revision of concepts of robot sensors, needed to understand later algorithms and nomenclatures. In the following items it is described the sensor fusion techniques, the robot localization problem, the map and robot representation alternatives, and the navigation problems for explored and non-explored environments. These information are important for a better understanding of the problem, on how represent it and how to evaluate the obtained results. After this introduction, it's described some SLAM algorithms, featuring in details the Grid Based FastSLAM. It's demonstrated by simulations that as high uncertainty about robot position, as large are the number of particles needed to maintain the generated map quality. This implies in a large computational cost, thus improving the uncertainty with sensor data fusion makes the robot work with less particles. It is also showed that choosing the right covariance in robot transition model is very important and finding a way to connect the covariance of sensor data fusion with SLAM can improve performance even more

    Techniques d'optimisation de la navigation globale d'un fauteuil roulant motorisé intelligent

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    RESUME Un projet de réalisation d’un prototype de fauteuil roulant motorisé intelligent (FRMI) avait réuni depuis quelques années les efforts de chercheurs et d'étudiants en cycle supérieur de trois universités du Québec: Ecole Polytechnique, Université de McGill et Université de Montréal. Ces efforts ont abouti à un fauteuil équipé de capteurs et d’un ensemble de modules permettant l’automatisation de nombreuses tâches. Il est ainsi un cas particulier de robot mobile. Malgré l’état avancé du fauteuil, plusieurs perspectives d’améliorations restent ouvertes. En effet, les modules de navigation globale existants se basent sur un algorithme de cartographie et localisation simultanées qui n’utilise qu’un modèle de perception adapté aux seules données des télémètres laser et qui présente quelques défauts dont la faible performance des capteurs utilisés pour certains types d’obstacles : les vitres et les objets transparents notamment sont difficiles à détecter avec des capteurs optiques comme les télémètres lasers. Le premier objectif principal du projet serait alors de proposer une solution à ce problème en partant du module de SLAM utilisé, the GMapping algorithm. On se propose également de proposer une alternative à la stratégie de cartographie par SLAM qui impose l'exploration des environnements visités. Notre second objectif consiste alors à utiliser les plans architecturaux pour générer automatiquement des grilles d'occupation compatibles avec le système de navigation du fauteuil. Notre réalisation de ces objectifs est exposée à travers l’étude du module à optimiser, l’analyse de ses composants et la conception des algorithmes-solutions. La première solution consiste essentiellement à intégrer un composant mieux adapté aux obstacles transparents et qui permet un enrichissement en temps réel par les détections des sonars de la carte en cours de construction par le GMapping. D'un autre côté, une technique toute nouvelle de construction de cartes est implémentée et testée grâce à un algorithme qui analyse les calques du plan d'architecture au format DXF afin d'extraire les éléments intéressants pour la navigation. Les résultats d'enrichissement montrent des cartes plus complètes où les obstacles transparents figurent aux bons emplacements. Nous avons pu profiter de la capacité des sonars à détecter les objets transparents sans pour autant perdre les hautes performances des télémètres laser et sans altérer la construction en temps réel de la carte accompagnée de la localisation. D'autre part, nos grilles d'occupation générées automatiquement ont été intégrées et utilisées avec succès dans le système de navigation. Une cartographie rapide et nettement plus économique est désormais possible pour le FRMI à condition d'avoir un plan au format DXF du milieu à modéliser.---------- ABSTRACT For the last few years, researchers and students of three universities of Quebec, École Polytechnique, Université de Montréal and McGill University, have been working on the creation of an Intelligent Powered Wheelchair (IPW). Thanks to their work, a wheelchair equipped with sensors and using a wide range of modules enabling the chair to do numerous automatic tasks became a reality. The IPW can therefore be considered as an example of a mobile robot. Many possible improvements still can be performed in spite of the very advanced state of the wheelchair. The global navigation is based on a simultaneous localization and mapping (SLAM) algorithm; the GMapping algorithm that uses a perception model conceived for lasers detections only and that is therefore under the required level of efficiency when mapping is held in environments containing transparent obstacles such as glass walls. Besides, the SLAM algorithm used is, as any other SLAM solution, based on a mapping strategy that imposes the environment exploration. Thus, our work's objectives are: First, to propose a solution to the transparent objects problem and second, to suggest a new alternative to the old exploration based strategy by exploiting blueprints to generate occupancy grid maps automatically. This report shows how we achieve these objectives through the study and analysis of the available SLAM algorithm and the conception of our solutions. In the first solution, we introduce a component that takes sonar detections as inputs and that draws in real time, based on a sonar perception model, the missing transparent objects on the GMapping map. In our second solution, a new technique makes use of an algorithm that analyses the layers of CAD blueprints given in a DXF file format in order to extract the important elements used for grids construction. Our algorithms proved to be efficient. In fact, we managed to get complete maps showing the transparent obstacles at the right spots. We managed then to make use of the sonar's capability to see transparent objects without causing the decrease of the GMapping performance in both mapping with laser data and localization. On the other hand, we managed to automatically build occupancy grid maps from blueprints. The built maps proved precise when used successfully in the wheeler's navigation system. We have now a faster and cheaper mapping solution for the IPW

    Recovering particle diversity in a Rao-Blackwellized particle filter for SLAM after actively closing loops

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    Acquiring models of the environment belongs to the fundamental tasks of mobile robots. Approaches addressing the problem of simultaneous localization and mapping (SLAM) typically process the perceived sensor data and do not influence the motion of the mobile robot In this paper, we present an approach to actively closing loops during exploration. It applies a Rao-Blackwellized particle filter to maintain multiple hypotheses about potential trajectories of the robot and corresponding maps. To prevent the particle filter from becoming overly confident, we present a technique to recover the particle diversity after successfully closing a loop. This way the particle depletion problem is avoided. The combination of our approach with the active loop closing strategy allows to deal with multiple nested loops. Experimental results presented in this paper illustrate the advantage of our method over pervious approaches to mapping with Rao-Blackwellized particle filters. © 2005 IEEE

    Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM After Actively Closing Loops

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