A Multiple-Hypothesis Approach to Concurrent Mapping and Localization for Autonomous Underwater Vehicles

This paper describes a multiple hypothesis approach to concurrent mapping and localization (CML) for autonomous underwater vehicles (AUVs). The objective of CML is to enable a mobile robot to build a map of an unknown environment, while simultaneously using that map to navigate with bounded position error. Multiple hypothesis concurrent mapping and localization (MHCML) has potential to provide a theoretically consistent framework that incorporates navigation error, sensor noise, data association uncertainty, and physically-based sensor models. MHCML is fundamentally different from conventional multiple hypothesis tracking because multiple hypotheses are considered for both the location of the vehicle and the locations of features. New techniques for evaluation of decision dependencies and calculation of likelihoods for vehicle and feature tracks are introduced. Simulation results are presented to illustrate the viability of the approach for an AUV equipped with a forward-look sonar.

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