Improved Particle Filter Based Localization and Mapping Techniques

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

Simultaneous Localization and Mapping in Repeating Environments

Master's thesis Mechatronics MAS500 - University of Agder 2018Konfidensiell til / confidential until 01.07.202

Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process

Analysing Large-scale Surveillance Video

Analysing large-scale surveillance video has drawn signi cant attention because drone technology and high-resolution sensors are rapidly improving. The mobility of drones makes it possible to monitor a broad range of the environment, but it introduces a more di cult problem of identifying the objects of interest. This thesis aims to detect the moving objects (mostly vehicles) using the idea of background subtraction. Building a decent background is the key to success during the process. We consider two categories of surveillance videos in this thesis: when the scene is at and when pronounced parallax exists. After reviewing several global motion estimation approaches, we propose a novel cost function, the log-likelihood of the student t-distribution, to estimate the background motion between two frames. The proposed idea enables the estimation process to be e cient and robust with auto-generated parameters. Since the particle lter is useful in various subjects, it is investigated in this thesis. An improvement to particle lters, combining near-optimal proposal and Rao-Blackwellisation, is discussed to increase the e ciency when dealing with non-linear problems. Such improvement is used to solve visual simultaneous localisation and mapping (SLAM) problems and we call it RB2-PF. Its superiority is evident in both simulations of 2D SLAM and real datasets of visual odometry problems. Finally, RB2-PF based visual odometry is the key component to detect moving objects from surveillance videos with pronounced parallax. The idea is to consider multiple planes in the scene to improve the background motion estimation. Experiments have shown that false alarms signi cantly reduced. With the landmark information, a ground plane can be worked out. A near-constant velocity model can be applied after mapping the detections on the ground plane regardless of the position and orientation of the camera. All the detection results are nally processed by a multi-target tracker, the Gaussian mixture probabilistic hypothesis density (GM-PHD) lter, to generate tracks

Indoor navigation systems for unmanned aerial vehicles

Ph.DDOCTOR OF PHILOSOPH

Localização e mapeamento eficiente para robótica : algoritmos e ferramentas

Doutoramento conjunto em InformáticaUm dos problemas fundamentais em robótica é a capacidade de estimar a pose de um robô móvel relativamente ao seu ambiente. Este problema é conhecido como localização robótica e a sua exatidão e eficiência têm um impacto direto em todos os sistemas que dependem da localização. Nesta tese, abordamos o problema da localização propondo um algoritmo baseado em scan matching com otimização robusta de mínimos quadrados não lineares em manifold com a utilização de um campo de verosimilhança contínuo como modelo de perceção. Esta solução oferece uma melhoria percetível na eficiência computacional sem perda de exatidão. Associado à localização está o problema de criar uma representação geométrica (ou mapa) do meio ambiente recorrendo às medidas disponíveis, um problema conhecido como mapeamento. No mapeamento a representação geométrica mais popular é a grelha volumétrica que discretiza o espaço em volumes cúbicos de igual tamanho. A implementação direta de uma grelha volumétrica oferece acesso direto e rápido aos dados mas requer uma quantidade substancial de memória. Portanto, propõe-se uma estrutura de dados híbrida, com divisão esparsa do espaço combinada com uma subdivisão densa do espaço que oferece tempos de acesso eficientes com alocações de memória reduzidas. Além disso, também oferece um mecanismo integrado de compressão de dados para reduzir ainda mais o uso de memória e uma estrutura de partilha de dados implícita que duplica dados, de forma eficiente, quando necessário recorrendo a uma estratégia copy-on-write. A implementação da solução descrita é disponibilizada na forma de uma biblioteca de software que oferece um framework para a criação de modelos baseados em grelhas volumétricas, e.g. grelhas de ocupação. Como existe uma separação entre o modelo e a gestão de espaço, todas as funcionalidades da abordagem esparsa-densa estão disponíveis para qualquer modelo implementado com o framework. O processo de mapeamento é um problema complexo considerando que localização e mapeamento são resolvidos simultaneamente. Este problema, conhecido como localização e mapeamento simultâneo (SLAM), tem tendência a de consumir recursos consideráveis à medida que a exigência na qualidade do mapeamento aumenta. De modo a contribuir para o aumento da eficiência, esta tese apresenta duas solução de SLAM. Na primeira abordagem, o algoritmo de localização é adaptado ao mapeamento incremental que, em combinação com o framework esparso-denso, oferece uma solução de SLAM online computacionalmente eficiente. O resultados obtidos são comparados com outras soluções disponíveis na literatura recorrendo a um benchmark de SLAM. Os resultados obtidos demonstram que a nossa solução oferece uma boa eficiência sem comprometer a exatidão. A segunda abordagem combina o nosso SLAM online com um filtro de partículas Rao-Blackwellized para propor uma solução de full SLAM com um grau elevado de eficiência computacional. A solução inclui propostas de distribuição melhorada com refinamento de pose através de scan matching, re-amostragem adaptativa com pesos de amostragem suavizados, partilha eficiente de dados entre partículas da mesma geração e suporte para multi-threading.One of the most basic perception problems in robotics is the ability to estimate the pose of a mobile robot relative to the environment. This problem is known as mobile robot localization and its accuracy and efficiency has a direct impact in all systems than depend on localization. In this thesis, we address the localization problem by proposing an algorithm based on scan matching with robust non-linear least squares optimization on a manifold that relies on a continuous likelihood field as measurement model. This solution offers a noticeable improvement in computational efficiency without losing accuracy. Associated with localization is the problem of creating the geometric representation (or map) of the environment using the available measurements, a problem known as mapping. In mapping, the most popular geometric representation is the volumetric grid that quantizes space into cubic volumes of equal size. The regular volumetric grid implementation offers direct and fast access to data but requires a substantial amount of allocated memory. Therefore, in this thesis, we propose a hybrid data structure with sparse division of space combined with dense subdivision of space that offers efficient access times with reduced memory allocation. Additionally, it offers an online data compression mechanism to further reduce memory usage and an implicit data sharing structure that efficiently duplicates data when needed using a thread safe copy-on-write strategy. The implementation of the solution is available as a software library that provides a framework to create models based on volumetric grids, e.g. occupancy grids. The separation between the model and space management makes all features of the sparse-dense approach available to every model implemented with the framework. The process of mapping is a complex problem, considering that localization and mapping have to be solved simultaneously. This problem, known as simultaneous localization and mapping (SLAM), has the tendency to consume considerable resources as the mapping quality requirements increase. As an effort to increase the efficiency of SLAM, this thesis presents two SLAM solutions. The first proposal adapts our localization algorithm to incremental mapping that, in combination with the sparse-dense framework, provides a computationally efficient online SLAM solution. Using a SLAM benchmark, the obtained results are compared with other solutions found in the literature. The comparison shows that our solution provides good efficiency without compromising accuracy. The second approach combines our online SLAM with a Rao-Blackwellized particle filter to propose a highly computationally efficient full SLAM solution. It includes an improved proposal distribution with scan matching pose refinement, adaptive resampling with smoothed importance weight, efficient sharing of data between sibling particles and multithreading support

Belief Functions: Theory and Algorithms

The subject of this thesis is belief function theory and its application in different contexts. Belief function theory can be interpreted as a generalization of Bayesian probability theory and makes it possible to distinguish between different types of uncertainty. In this thesis, applications of belief function theory are explored both on a theoretical and on an algorithmic level. The problem of exponential complexity associated with belief function inference is addressed in this thesis by showing how efficient algorithms can be developed based on Monte-Carlo approximations and exploitation of independence. The effectiveness of these algorithms is demonstrated in applications to particle filtering, simultaneous localization and mapping, and active classification

Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop

Contributions to Localization, Mapping and Navigation in Mobile Robotics

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