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
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Collaborative Multi-robot Localization
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Consistent cooperative localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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
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Statistical algorithms using particle filters for collaborative multi-robot localization have been proposed. In these algorithms, by synchronizing every robot's belief or exchanging particles of the robots with each other, fast and accurate localization is attained. These algorithms assume correct recognition of other robots, and the effects of recognition errors are not discussed. However, if the recognition of other robots is incorrect, a large amount of error in localization can occur. This article describes this problem. Furthermore, an algorithm for collaborative multi-robot localization is proposed in order to cope with this problem. In the proposed algorithm, the particles of a robot are sent to other robots according to measurement results obtained by the sending robot. At the same time, some particles remain in the sending robot. Particles received from other robots are evaluated using measurement results obtained by the receiving robot. The proposed method is tolerant to recognition error by the remaining particles and evaluating the exchanged particles in the sending and receiving robots twice, and if there is no recognition error, the proposed method increases the accuracy of the estimation by these two evaluations. These properties of the proposed method are argued mathematically. Simulation results show that incorrect recognition of other robots does not cause serious problems in the proposed method.