Dynamic sensory probabilistic maps for mobile robot localization

Abstract

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 configuration 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 configurations. We propose a combined algorithm for map update and robot localization.