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1999 | Buch

Mobile Robot Localization and Map Building

A Multisensor Fusion Approach

verfasst von: José A. Castellanos, Juan D. Tardós

Verlag: Springer US

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
During the last decade, many researchers have dedicated their efforts to construct revolutionary machines and to provide them with some kind of artificial intelligence to perform some of the most disgusting, risky or monotonous tasks historically assigned to human beings. Among those machines, mobile robots are undoubtedly in the cutting-edge of the current research directions. An initial classification of mobile robots can be immediately 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.
José A. Castellanos, Juan D. Tardós
Chapter 2. Uncertain Geometric Information
Abstract
Successful operation of a mobile robot in the navigation area highly depends on the environmental information gathered by its exteroceptive sensors such as laser rangefinder, ultrasonic sensors, CCD cameras, etc. Typically, geometric information derives from the surface of objects in the environment, such as planes, edges, vertices, etc. In general, only partial and imprecise information can be obtained from real sensors, thus integration of multiple observations of the same geometric entity is required to compute an estimation of its location with respect to the vehicle. Partiality refers to the degrees of freedom associated to the representation of different geometric entities and how they determine the location of other entities related to them. Imprecision refers to the accuracy in the estimation of the location of geometric entities. Adequately representing such environmental information is a crucial aspect in mobile robotics. Dealing with uncertain geometric information has given rise to a variety of models which can be classified into either set-based models or probabilistic-models. On the one hand, set-based models describe the imprecision in the localization of a geometric entity by a bounded region, which correspond to the set of feasible locations for the geometric entity. Fusion of geometric information is performed using algebraic operations on the regions limited by the error bounds. On the other hand, probabilistic models represent the uncertain location of a geometric element using a probability distribution, usually Gaussian. Here, fusion is carried out using estimation methods.
José A. Castellanos, Juan D. Tardós
Chapter 3. Segment-based Representation of Indoor Environments
Abstract
Selecting the paradigm to represent environmental information has been a subject of discussion since exteroceptive sensors were available. The problem consists in searching for an appropriate representation paradigm able to describe the environment as precisely as possible. Clearly, this paradigm depends on the application, the intrinsic characteristics of the navigation area and the exteroceptive sensors being used.
José A. Castellanos, Juan D. Tardós
Chapter 4. Detecting High-Level Features by Multisensor Fusion
Abstract
Robust sensing of the environment of a mobile robot is an important task both in localizing the robot and in building a complete and reliable map of such an environment. One of the fundamental ideas to achieve this robustness is the use of redundancy, that is, to combine environmental information obtained by several sensors. Such an approach provides more reliable and accurate information about what the sensory system of the robot really observes. Credibility of the observed features is therefore enhanced. Dealing with redundancy requires both the availability of a robust modeling tool to represent uncertain geometric information and a multisensor fusion mechanism capable of handling information obtained by different sensors.
José A. Castellanos, Juan D. Tardós
Chapter 5. The First-Location Problem
Abstract
Mobile robot localization consists in the problem of determining the relative transformation between the reference frame attached to the robot R and a base reference frame W (figure 5.1). In two dimensions this relative transformation is given by a translation in the horizontal plane (motion plane) and a rotation around a vertical axis:
$$t_{WR} = Trans(x_{WR} ,y_{WR} )Rot(Z,\varphi _{WR} )$$
(1)
Typically, this relative transformation is obtained by matching the current observations gathered by the exteroceptive sensors mounted on the robot, with model features stored in a database representing a priori knowledge about the navigation environment.
José A. Castellanos, Juan D. Tardós
Chapter 6. Simultaneous Localization and Map Building
Abstract
Successful path planning and navigation of a mobile robot in an indoor environment requires the availability of both a sufficiently reliable estimation of the current vehicle location, and a sufficiently precise map of the navigation area. A priori model maps are rarely available and when they are, they usually introduce inaccuracies in the planning tasks. As reported in the literature [Neira 97] solutions based on a priori maps proved unsatisfactory mainly due to their incompleteness, incorrectness and imprecision. Therefore, an automatic construction of the map of the environment in which the robot navigates is desirable, and it has become an important research direction in today’s robotics community.
José A. Castellanos, Juan D. Tardós
Chapter 7. Conclusions
Abstract
This work has described our recent research on the problems of localization and map building for a wheeled-based mobile robot which navigates in a human-made indoor environment. The mobile robot was equipped with a multisensor system composed of a laser rangefinder and a monocular vision system.
José A. Castellanos, Juan D. Tardós
Backmatter
Metadaten
Titel
Mobile Robot Localization and Map Building
verfasst von
José A. Castellanos
Juan D. Tardós
Copyright-Jahr
1999
Verlag
Springer US
Electronic ISBN
978-1-4615-4405-0
Print ISBN
978-1-4613-6982-0
DOI
https://doi.org/10.1007/978-1-4615-4405-0