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Maximum likelihood estimation of a sensor configuration in a polygonal environment

A new approach is described for estimating the sensor configuration of a mobile robot, given a set of range data in a known environment. A robot is equipped with multiple sensors. The environment is represented by a set of line segments in any plane. The perceptual equivalence classes of the sensor configuration space (x, y, /spl theta/) are pre-computed. Two sensor configurations are considered equivalent if the mapping from the sensors to the visible line segments is identical. When a set of range data is observed at an execution time, a searching process is invoked in energy equivalence class. Since the mapping of the sensors to obstacles is constant in a class, the objective function for maximum likelihood estimation behaves well. An efficient algorithm to search for the minima is presented. A simulation using randomly generated sensor data in randomly created robot environments is shown.