Tracking and data association
The interactive museum tour-guide robot
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Position estimation for mobile robots in dynamic environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
People tracking with anonymous and ID-sensors using Rao-Blackwellised particle filters
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A novel system for tracking pedestrians using multiple single-row laser-range scanners
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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This paper presents a novel approach to mobile robot localization. This approach is basically different from other methods developed so far, because the sensors are not mounted on the mobile robot. In fact, they are statically located in the environment. These sensors, which could be for instance SICK lasers embedded in the walls of a museum, constantly measure the distance to points on the surface of the mobile robot in a plane. The only further assumption we have to make is that the robot has a circular shape in this plane. This supposition is feasible, since most indoor service robots have a circular shape. We then use a simple tracking algorithm based on the Kalman filter and geometrical considerations to localize the robot with a very high precision. In simulations it is shown, that our method is able to localize the robot with an error in the range of one centimeter.