On the identification of sonar features

We are interested in inferring the sources of various types of sonar features typically observed by a mobile robot. After a brief discussion of terrestrial sonar sensing, we develop a set of operators that associates arc-shaped features extracted from sonar scans with real world primitives. Our classification scheme is probabilistic and is based on empirical data: the confidence of the association hypotheses produced by the operators is evaluated statistically. Some of our experimental results suggest that methods based on models of perfect sonar sensors may not be completely consistent with observed data. The management and merging of a collection of hypotheses concerning various sonar features allows the system to produce a coherent and mutually-compatible set of inferences for the entire observed environment.