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

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 are planes, 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 derived from a pair of knife edged unloaded encoder wheels. Discrete sonar observations are incrementally merged into partial planes to produce a realistic representation of environment that is amenable to sonar localisation. Collinearity constraints among features are exploited to enhance both the map feature estimation and robot localisation. The map update employs an Iterated Extended Kalman Filter (IEKF) in the first implementation and subsequently a comparison is made with the JulierUhlmann Kalman Filter (JUKF) which improves the accuracy of covariance propagation when non-linear equations are involved. The map accounts for correlation among features and robot positions. Partial planes are 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.