Probabilistic Robot Localization Using Visual Landmarks

Effective robot navigation and route planning is impossible unless the position of the robot within its environment is known. Motion sensors that track the relative movement of a robot are inherently unreliable, so it is necessary to use cues from the external environment to periodically localize the robot. In this study I examine the feasibility of using the probabilistic Monte Carlo localization algorithm to estimate a robot's location based off of occasional visual landmark cues. To demonstrate this, I designed a robot capable of localizing within Olin-Rice by observing pieces of colored paper placed at regular intervals along the halls.

As mobile robots become more common in general knowledge and practices, as opposed to simply in research labs, there is an increased need for the introduction and methods to Simultaneous Localization and Mapping (SLAM) and its techniques and concepts related to robotics. Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods investigates the complexities of the theory of probabilistic localization and mapping of mobile robots as well as providing the most current and concrete developments. This reference source aims to be useful for practitioners, graduate and postgraduate students, and active researchers alike.

An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

The use of vision in mobile robotics in one of the main goal of this thesis. In particular novel appearance based approaches for image matching metric are introduced. These approaches are applied to the problem of mobile robot localization.Similarity measures between robot's views are used in probabilistic methods for robot pose estimation. In this field of probabilistic localization active approach are proposed allowing the robot to faster and better localize. All methods have been extensively tested using a real robot in an indoor environment.Note: the book is the publication of the PhD thesis discussed in Università Politecnica delle Marche, Ancona, Italy in 2006 by Emanuele Frontoni

This paper presents a robot self-localization method based on visual attention. This method takes advantage of the saliency-based model of attention to automatically learn configurations of salient visual landmarks along a robot path. During navigation, the visual attention algorithms detect a set of conspicuous visual features which are compared with the learned landmark configurations in order to determine the robot position on the navigation path. More specifically, the multi-cue attention model detects the most salient visual features that are potential candidates for landmarks. These features are then characterized by a visual descriptor vector computed from various visual cues and at different scales. By tracking the detected features over time, our landmarks selection procedure automatically evaluates their robustness and retains only the most robust features as landmarks. Further, the selected landmarks are organized into a topological map that is used for self-localization during the navigation phase. The self-localization method is based on matching between the currently detected visual features configuration and the configurations of the learned landmarks. Indeed, the matching procedure yields a probabilistic measure of the whereabouts of the robot. Thanks to the multi-featured input of the attention model, our method is potentially able to deal with a wide range of navigation environments.

During the last decade, many researchers have dedicated their efforts to constructing revolutionary machines and to providing them with forms of artificial intelligence to perform some of the most hazardous, risky or monotonous tasks historically assigned to human beings. Among those machines, mobile robots are undoubtedly at the cutting edge of current research directions. A rough classification of mobile robots can be considered: on the one hand, mobile robots oriented to human-made indoor environments; on the other hand, mobile robots oriented to unstructured outdoor environments, which could include flying oriented robots, space-oriented robots and underwater robots. The most common motion mechanism for surface mobile robots is the wheel-based mechanism, adapted both to flat surfaces, found in human-made environments, and to rough terrain, found in outdoor environments. However, some researchers have reported successful developments with leg-based mobile robots capable of climbing up stairs, although they require further investigation. The research work presented here focuses on wheel-based mobile robots that navigate in human-made indoor environments. The main problems described throughout this book are: Representation and integration of uncertain geometric information by means of the Symmetries and Perturbations Model (SPmodel). This model combines the use of probability theory to represent the imprecision in the location of a geometric element, and the theory of symmetries to represent the partiality due to characteristics of each type of geometric element. A solution to the first location problem, that is, the computation of an estimation for the mobile robot location when the vehicle is completely lost in the environment. The problem is formulated as a search in an interpretation tree using efficient matching algorithms and geometric constraints to reduce the size of the solution space. The book proposes a new probabilistic framework adapted to the problem of simultaneous localization and map building for mobile robots: the Symmetries and Perturbations Map (SPmap). This framework has been experimentally validated by a complete experiment which profited from ground-truth to accurately validate the precision and the appropriateness of the approach. The book emphasizes the generality of the solutions proposed to the different problems and their independence with respect to the exteroceptive sensors mounted on the mobile robot. Theoretical results are complemented by real experiments, where the use of multisensor-based approaches is highlighted.

Localization and mapping are the essence of successful navigation in mobile platform technology. Localization is a fundamental task in order to achieve high levels of autonomy in robot navigation and robustness in vehicle positioning. Robot localization and mapping is commonly related to cartography, combining science, technique and computation to build a trajectory map that reality can be modelled in ways that communicate spatial information effectively. This book describes comprehensive introduction, theories and applications related to localization, positioning and map building in mobile robot and autonomous vehicle platforms. It is organized in twenty seven chapters. Each chapter is rich with different degrees of details and approaches, supported by unique and actual resources that make it possible for readers to explore and learn the up to date knowledge in robot navigation technology. Understanding the theory and principles described in this book requires a multidisciplinary background of robotics, nonlinear system, sensor network, network engineering, computer science, physics, etc.

This paper reports a landmark-based localization method relying on visual attention. In a learning phase, the multi-cue, multi-scale saliency-based model of visual attention is used to automatically acquire robust visual landmarks that are integrated into a topological map of the navigation environment. During navigation, the same visual attention model detects the most salient visual features that are then matched to the learned landmarks. The matching result yields a probabilistic measure of the current location of the robot. Further, this measure is integrated into a more general Markov localization framework in order to take into account the structural constraints of the navigation environment, which significantly enhances the localization results. Some experiments carried out with real training and test image sequences taken by a robot in a lab environment show the potential of the proposed method.

Probabilistic Reasoning and Decision Making in Sensory-Motor Systems by Pierre Bessiere, Christian Laugier and Roland Siegwart provides a unique collection of a sizable segment of the cognitive systems research community in Europe. It reports on contributions from leading academic institutions brought together within the European projects Bayesian Inspired Brain and Artifact (BIBA) and Bayesian Approach to Cognitive Systems (BACS). This fourteen-chapter volume covers important research along two main lines: new probabilistic models and algorithms for perception and action, new probabilistic methodology and techniques for artefact conception and development. The work addresses key issues concerned with Bayesian programming, navigation, filtering, modelling and mapping, with applications in a number of different contexts.

This monograph describes a new family of algorithms for the simultaneous localization and mapping (SLAM) problem in robotics, called FastSLAM. The FastSLAM-type algorithms have enabled robots to acquire maps of unprecedented size and accuracy, in a number of robot application domains and have been successfully applied in different dynamic environments, including a solution to the problem of people tracking.