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
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
A near-tight approximation lower bound and algorithm for the kidnapped robot problem
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Robust Position Tracking for Mobile Robots with Adaptive Evolutionary Particle Filter
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Active mobile robot localization
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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Mobile robot localization is the problem of determining the position of a mobile robot from sensor data. Active localization provides setting the robot's motion direction and determining the pointing direction of the sensors during localization so as to most efficiently localize the robot. This paper proposes an active localization approach that employs Monte Carlo Localization, which is based on particle filters. The technique offers two main advantages. 1) The framework applies a different way of initializing the particles that helps to reduce some steps of localization, and 2) a new resampling scheme is used to reduce the cost of localization and solve the kidnapped robot problem. Experimental results show that the probability of robot successfully localize itself is considerably high, i.e. robot can recover from failure and localize itself based on new sensor data and reduction of cost is noticeable.