A hybrid approach to simultaneous localization and mapping in indoors environment

This thesis will present SLAM in the current literature to benefit from then it will present the investigation results for a hybrid approach used where different algorithms using laser, sonar, and camera sensors were tested and compared. The contribution of this thesis is the development of a hybrid approach for SLAM that uses different sensors and where different factors are taken into consideration such as dynamic objects, and the development of a scalable grid map model with new sensors models for real time update of the map.The thesis will show the success found, difficulties faced and limitations of the algorithms developed which were simulated and experimentally tested in an indoors environment

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Wo bin ich? Beiträge zum Lokalisierungsproblem mobiler Roboter

Self-localization addresses the problem of estimating the pose of mobile robots with respect to a certain coordinate system of their workspace. It is needed for various mobile robot applications like material handling in industry, disaster zone operations, vacuum cleaning, or even the exploration of foreign planets. Thus, self-localization is a very essential capability. This problem has received considerable attention over the last decades. It can be decomposed into localization on a global and local level. Global techniques are able to localize the robot without any prior knowledge about its pose with respect to an a priori known map. In contrast, local techniques aim to correct so-called odometry errors occurring during robot motion. In this thesis, the global localization problem for mobile robots is mainly addressed. The proposed method is based on matching an incremental local map to an a priori known global map. This approach is very time and memory efficient and robust to structural ambiguity as well as with respect to the occurrence of dynamic obstacles in non-static environments. The algorithm consists of several components like ego motion estimation or global point cloud matching. Nowadays most computers feature multi-core processors and thus map matching is performed by applying a parallelized variant of the Random Sample Matching (pRANSAM) approach originally devised for solving the 3D-puzzle problem. pRANSAM provides a set of hypotheses representing alleged robot poses. Techniques are discussed to postprocess the hypotheses, e.g. to decide when the robot pose is determined with a sufficient accuracy. Furthermore, runtime aspects are considered in order to facilitate localization in real-time. Finally, experimental results demonstrate the robustness of the method proposed in this thesis.Das Lokalisierungsproblem mobiler Roboter beschreibt die Aufgabe, deren Pose bezüglich eines gegebenen Weltkoordinatensystems zu bestimmen. Die Fähigkeit zur Selbstlokalisierung wird in vielen Anwendungsbereichen mobiler Roboter benötigt, wie etwa bei dem Materialtransport in der industriellen Fertigung, bei Einsätzen in Katastrophengebieten oder sogar bei der Exploration fremder Planeten. Eine Unterteilung existierender Verfahren zur Lösung des genannten Problems erfolgt je nachdem ob eine Lokalisierung auf lokaler oder auf globaler Ebene stattfindet. Globale Lokalisierungsalgorithmen bestimmen die Pose des Roboters bezüglich eines Weltkoordinatensystems ohne jegliches Vorwissen, wohingegen bei lokalen Verfahren eine grobe Schätzung der Pose vorliegt, z.B. durch gegebene Odometriedaten des Roboters. Im Rahmen dieser Dissertation wird ein neuer Ansatz zur Lösung des globalen Lokalisierungsproblems vorgestellt. Die grundlegende Idee ist, eine lokale Karte und eine globale Karte in Übereinstimmung zu bringen. Der beschriebene Ansatz ist äußerst robust sowohl gegenüber Mehrdeutigkeiten der Roboterpose als auch dem Auftreten dynamischer Hindernisse in nicht-statischen Umgebungen. Der Algorithmus besteht hauptsächlich aus drei Komponenten: Einem Scanmatcher zur Generierung der lokalen Karte, einer Methode zum matchen von lokaler und globaler Karte und einer Instanz, welche entscheidet, wann der Roboter mit hinreichender Sicherheit korrekt lokalisiert ist. Das Matching von lokaler und globaler Karte wird dabei von einer parallelisierten Variante des Random Sample Matching (pRANSAM) durchgeführt, welche eine Menge von Posenhypothesen liefert. Diese Hypothesen werden in einem weiteren Schritt analysiert, um bei hinreichender Eindeutigkeit die korrekte Roboterpose zu bestimmen. Umfangreiche Experimente belegen die Zuverlässigkeit und Genauigkeit des in dieser Dissertation vorgestellten Verfahrens

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

Visual location awareness for mobile robots using feature-based vision

Department Head: L. Darrell Whitley.2010 Spring.Includes bibliographical references (pages 48-50).This thesis presents an evaluation of feature-based visual recognition paradigm for the task of mobile robot localization. Although many works describe feature-based visual robot localization, they often do so using complex methods for map-building and position estimation which obscure the underlying vision systems' performance. One of the main contributions of this work is the development of an evaluation algorithm employing simple models for location awareness with focus on evaluating the underlying vision system. While SeeAsYou is used as a prototypical vision system for evaluation, the algorithm is designed to allow it to be used with other feature-based vision systems as well. The main result is that feature-based recognition with SeeAsYou provides some information but is not strong enough to reliably achieve location awareness without the temporal context. Adding a simple temporal model, however, suggests a more reliable localization performance

Visual Place Recognition for Autonomous Robots

Autonomous robotics has been the subject of great interest within the research community over the past few decades. Its applications are wide-spread, ranging from health-care to manufacturing, goods transportation to home deliveries, site-maintenance to construction, planetary explorations to rescue operations and many others, including but not limited to agriculture, defence, commerce, leisure and extreme environments. At the core of robot autonomy lies the problem of localisation, i.e, knowing where it is and within the robotics community, this problem is termed as place recognition. Place recognition using only visual input is termed as Visual Place Recognition (VPR) and refers to the ability of an autonomous system to recall a previously visited place using only visual input, under changing viewpoint, illumination and seasonal conditions, and given computational and storage constraints. This thesis is a collection of 4 inter-linked, mutually-relevant but branching-out topics within VPR: 1) What makes a place/image worthy for VPR?, 2) How to define a state-of-the-art in VPR?, 3) Do VPR techniques designed for ground-based platforms extend to aerial platforms? and 4) Can a handcrafted VPR technique outperform deep-learning-based VPR techniques? Each of these questions is a dedicated, peer-reviewed chapter in this thesis and the author attempts to answer these questions to the best of his abilities. The worthiness of a place essentially refers to the salience and distinctiveness of the content in the image of this place. This salience is modelled as a framework, namely memorable-maps, comprising of 3 conjoint criteria: a) Human-memorability of an image, 2) Staticity and 3) Information content. Because a large number of VPR techniques have been proposed over the past 10-15 years, and due to the variation of employed VPR datasets and metrics for evaluation, the correct state-of-the-art remains ambiguous. The author levels this playing field by deploying 10 contemporary techniques on a common platform and use the most challenging VPR datasets to provide a holistic performance comparison. This platform is then extended to aerial place recognition datasets to answer the 3rd question above. Finally, the author designs a novel, handcrafted, compute-efficient and training-free VPR technique that outperforms state-of-the-art VPR techniques on 5 different VPR datasets

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

On Consistent Mapping in Distributed Environments using Mobile Sensors

The problem of robotic mapping, also known as simultaneous localization and mapping (SLAM), by a mobile agent for large distributed environments is addressed in this dissertation. This has sometimes been referred to as the holy grail in the robotics community, and is the stepping stone towards making a robot completely autonomous. A hybrid solution to the SLAM problem is proposed based on "first localize then map" principle. It is provably consistent and has great potential for real time application. It provides significant improvements over state-of-the-art Bayesian approaches by reducing the computational complexity of the SLAM problem without sacrificing consistency. The localization is achieved using a feature based extended Kalman filter (EKF) which utilizes a sparse set of reliable features. The common issues of data association, loop closure and computational cost of EKF based methods are kept tractable owing to the sparsity of the feature set. A novel frequentist mapping technique is proposed for estimating the dense part of the environment using the sensor observations. Given the pose estimate of the robot, this technique can consistently map the surrounding environment. The technique has linear time complexity in map components and for the case of bounded sensor noise, it is shown that the frequentist mapping technique has constant time complexity which makes it capable of estimating large distributed environments in real time. The frequentist mapping technique is a stochastic approximation algorithm and is shown to converge to the true map probabilities almost surely. The Hybrid SLAM software is developed in the C-language and is capable of handling real experimental data as well as simulations. The Hybrid SLAM technique is shown to perform well in simulations, experiments with an iRobot Create, and on standard datasets from the Robotics Data Set Repository, known as Radish. It is demonstrated that the Hybrid SLAM technique can successfully map large complex data sets in an order of magnitude less time than the time taken by the robot to acquire the data. It has low system requirements and has the potential to run on-board a robot to estimate large distributed environments in real time

Optimal Wheelchair Multi-LiDAR Placement for Indoor SLAM

One of the most prevalent technologies used in modern robotics is Simultaneous Localization and Mapping or, SLAM. Modern SLAM technologies usually employ a number of different probabilistic mathematics to perform processes that enable modern robots to not only map an environment but, also, concurrently localize themselves within said environment. Existing open-source SLAM technologies not only range in the different probabilistic methods they employ to achieve their task but, also, by how well the task is achieved and by their computational requirements. Additionally, the positioning of the sensors in the robot also has a substantial effect on how well these technologies work. Therefore, this dissertation is dedicated to the comparison of existing open-source ROS implemented 2D SLAM technologies and in the maximization of the performance of said SLAM technologies by researching optimal sensor placement in a Intelligent Wheelchair context, using SLAM performance as a benchmark