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This monograph is a revised version of the D.Phil. thesis of the first author, submitted in October 1990 to the University of Oxford. This work investigates the problem of mobile robot navigation using sonar. We view model-based navigation as a process of tracking naturally occurring environment features, which we refer to as “targets”.

Targets that have been predicted from the environment map are tracked to provide vehicle position estimates. Targets that are observed, but not predicted, represent unknown environment features or obstacles, and cause new tracks to be initiated, classified, and ultimately integrated into the map.

Chapter 1 presents a brief definition of the problem and a discussion of the basic research issues involved. No attempt is made to survey exhaustively the mobile robot navigation literature, the reader is strongly encouraged to consult other sources.

Chapter 2 provides a detailed sonar sensor model. A good sensor model is a crucial component of our approach to navigation, and is used both for predicting expected observations and classifying unexpected observations. Kuc and Siegel first reported that in a specular environment, sonar data should take the form of circular arcs in Cartesian coordinates.

Chapter 3 presents an algorithm for model-based localization that is based on an extended Kalman filter (EKF) that utilizes matches between observed RCDs and RCDs predicted from an a priori map to update position. The algorithm has been implemented on several different robots, using real data. Localization runs in two different laboratories are presented.

In Chapter 4, the discussion turns to the map building problem. We view map building as a process of initiating tracks for events unexplainable in terms of the environment map. Experimental results are obtained for the restricted case of learning an unknown room from precisely known vehicle locations. A simple office scene is mapped to sub-centimeter accuracy.

Chapter 5 presents a unified approach to navigation, in which the multiple requirements of localization, obstacle avoidance, and map building can be simultaneously addressed in a common multitarget tracking framework. Localization while learning and map maintenance in a dynamic environment are discussed.

Chapter 6 describes the concept of directed sensing. The slow data acquisition speed of acoustic sensing makes practical sonar-based navigation difficult to achieve. To overcome this problem, we believe that directed sensing strategies can be used to achieve fast, continuous operation. By tracking environment targets as the vehicle moves, less data needs to be acquired, and the need to re-solve the correspondence problem at each iteration of the perception cycle is obviated.

Chapter 7 assesses sonar’s potential in comparison with other range sensing modalities and discusses an agenda for future research.

Appendix A provides hardware and software details for the implementations.

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