NDLI: Dynamic sensory probabilistic maps for mobile robot localization

Content Provider	IEEE Xplore Digital Library
Author	Vlassis, N.A. Papakonstantinou, G. Tsanakas, P.
Copyright Year	1998
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Greece (Vlassis, N.A.)
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.
Starting Page	718
Ending Page	723
File Size	556250
Page Count	6
File Format	PDF
ISBN	0780344650
DOI	10.1109/IROS.1998.727276
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-10-17
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mobile robots Robot sensing systems Robot localization Uncertainty Hidden Markov models Kernel Navigation Maximum likelihood estimation Q measurement Sensor phenomena and characterization
Content Type	Text
Resource Type	Article

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