Title :
Dynamic sensory probabilistic maps for mobile robot localization
Author :
Vlassis, N.A. ; Papakonstantinou, G. ; Tsanakas, P.
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Greece
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
Keywords :
mobile robots; navigation; probability; robot vision; dynamic sensory probabilistic maps; map update; mobile robot localization; probability density function; sensor density; Hidden Markov models; Kernel; Maximum likelihood estimation; Mobile robots; Navigation; Q measurement; Robot localization; Robot sensing systems; Sensor phenomena and characterization; Uncertainty;
Conference_Titel :
Intelligent Robots and Systems, 1998. Proceedings., 1998 IEEE/RSJ International Conference on
Conference_Location :
Victoria, BC
Print_ISBN :
0-7803-4465-0
DOI :
10.1109/IROS.1998.727276