Localization in ambiguous environments using multiple weak cues

This paper presents a probabilistic approach for sensor-based localization with weak sensor data. Wireless received signal strength measurements are used to disambiguate sonar measurements in symmetric environments. Particle filters are used to model the multi-hypothesis estimation problem. Experiments indicate that multiple weak cues can provide robust position estimates and that multiple sensors also aid in solving the kidnapped robot problem.