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Abstract

This paper describes a mobile robot equipped with a sonar sensor array in a guided, feature-based map-building task in an indoor environment. The landmarks common to indoor environments areplanes, corners, and edges, and these are located and classified with the sonar sensor array The map-building process makes use of accurate odometry information that is derivedfiom apair ofknife-edged unloaded encoder wheels. Discrete sonar observations are incrementally merged into partial planes to produce a realistic representation of the environment that is amenable to sonar localization. Collinearity constraints among featurs ar exploited to enhance both the map-featwe estimation and robot localization. The map update employs an iterated extended Kalmanfilter in the first implementation and subsequently a comparison is made with the Julier-Uhlmann-Durrant-Whyte Kalman filter which improves the accuracy of covariance propagation when nonlinear equations are involved. The map accounts for correlation among features and robot positions. Partial planes am also used to eliminate phantom targets caused by specular reflection of the sonar. Unclassifiable sonar targets are integrated into the map for the purpose of obstacle avoidance. The paper presents simulated and experimental data.

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Mobile-Robot Map Building from an Advanced Sonar Array and Accurate Odometry

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