An efficient area-based observation model for monte-carlo robot Localization

The problem of mobile robot self-localization is considered as solved since Thrun's et. al pioneering work using monte-carlo filters for robot Localization (MCL). However, MCL is robust and precise under constraints like completely known environments and the sensor data must contain enough ¿true data¿ as contained in the map. In fact these conditions cannot always be guaranteed, which may results in a poor accuracy of the localization. In this paper we present a area-based observation model that is applied to MCL self-localization. The model is based on the idea of tracking the ground area inside the ¿free space¿ (not occupied cells) of a known map. Experimental data shows that the proposed model improves the robustness and accuracy of laser and stereo vision sensors under certain conditions like incomplete map, limited FOV and limited range of sensing. We also present an efficient approximation of our sensor model based on integral images.