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
Monte Carlo Localization with Mixture Proposal Distribution
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Robust global localization using clustered particle filtering
Eighteenth national conference on Artificial intelligence
The Reverse Monte Carlo localization algorithm
Robotics and Autonomous Systems
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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Mobile robot localization is a fundamental and very important problem in robotics. Grid localization and Monte Carlo localization (MCL) are two of the most widely used approaches for localization, especially the MCL. In this paper, we propose a novel global localization algorithm called moving grid cell based MCL, which takes advantages of both grid localization and MCL and overcomes their respective shortcomings. The proposed algorithm can reduce the computational cost, yield good results in highly symmetric environment and help avoid the kidnapping problem. Experimental results demonstrate the effectiveness and advantages of the proposed algorithm.