Dynamic sensory probabilistic maps for mobile robot localization

In order to localize itself, a mobile robot tries to match its sensory information at any instant against a prior environment model, the map. A probabilistic map can be regarded as a model that stores at each robot connguration q the probability density function of the sensor readings at q. By combining the knowledge of its current position, the new-coming sensory information, and the probabilistic map the robot is capable of improving its prior position estimate. In this paper we propose a novel sensor model and a method for maintaining a probabilistic map in cases of dynamic environments. When the environment structure changes, the map must adapt to this change by modifying the sensor densities at the respective conng-urations. We propose a combined algorithm for map update and robot localization.