Directed Sonar Sensing for Mobile Robot Navigation: (175 The Springer International Series in Engineering and Computer Science)

While many mobile robots come equipped with ultrasonic range sensing (sonar), accurate map building and position estimation using sonar has been elusive because of the difficulty in interpreting sonar data correctly. This book presents evidence that sonar can in fact fulfil the perception role for the provision of long-term autonomous navigation in a broad class of man-made environments. A new approach to mobile robot navigation is presented that unifies the problems of localization, obstacle detection and map building in a combined multi-target tracking framework. The primary tools of this approach are the Kalman filter and a physically based sonar sensing model. Experimental results with real sonar data demonstrate model-based localization using an a-priori hand-measured map and sub-centimetre accuracy map building for an uncluttered office scene. This book shoud be of particular interest to researchers on mobile robotics, especially potential users of sonar. The approach has greater significance, however, as the issues involved - the choice of representation, the problem of data association and the pursuit of long-tem autonomy - are central to many outstanding problems in robotics and artificial intelligence.

Table of Contents:
Preface.- 1 Introduction.- 1.1 The Navigation Problem.- 1.2 Why Use Sonar?.- 1.3 Choosing a Representation.- 1.4 The Kalman Filter.- 1.5 Data Association.- 1.6 Overview.- 2 A Sonar Sensor Model.- 2.1 Introduction.- 2.2 Previous Work.- 2.3 Terminology.- 2.4 The Physics of Sonar.- 2.5 Predicting Sonar Data.- 2.6 Theory vs Practice.- 2.7 Regions of Constant Depth.- 2.8 Sparse vs Densely Sampled Data.- 2.9 Discussion.- 2.10 Summary.- 3 Model-based Localization.- 3.1 Introduction.- 3.2 Problem Statement.- 3.3 The Basic Localization Cycle.- 3.4 Algorithm Summary.- 3.5 Off-line Processing of Densely Sampled Data.- 3.6 Sparse Data Error Model.- 3.7 Tracking Planar Targets.- 3.8 Tracking Planes, Corners, and Cylinders.- 3.9 Hands-off Localization Results.- 3.10 Discussion.- 3.11 Alternative Approaches.- 3.12 Summary.- 4 Map Building.- 4.1 Introduction.- 4.2 Specular Event Interpretation.- 4.3 Rules for RCD-based Sonar Interpretation.- 4.4 Map Building via Track Initiation.- 4.5 Experimental Results.- 4.6 Multiple Hypothesis Track Initiation.- 4.7 Alternative Approaches.- 4.8 Why Build Accurate Maps?.- 5 Simultaneous Map Building and Localization.- 5.1 A Unified Approach to Navigation.- 5.2 Research Issues.- 5.3 Restatement of the problem.- 5.4 A Strategy for Decoupling A(k I k).- 5.5 Initialization With Sonar.- 5.6 Dynamic Map Building and Maintenance.- 5.7 Summary.- 6 Directed Sensing Strategies.- 6.1 The Time Problem.- 6.2 Tracking Sonars.- 6.3 Orienteering.- 6.4 Related Research: Sensor Control.- 6.5 Summary.- 7 Why Use Sonar?.- 7.1 Sonar vs the Infrared Rangefinder.- 7.2 Sonar vs Vision.- 7.3 Sensor Fusion.- 7.4 Future Research.- 7.5 Contributions.- A Hardware and Software.- A.1 Mobile Robots.- A.2 The Polaroid Ultrasonic Ranging System.- A.3 Software.