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
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Scalable navigation system for mobile robots based on the agent dual-space control paradigm
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Multi-observation sensor resetting localization with ambiguous landmarks
Autonomous Robots
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Self-localization is a deeply investigated field in mobile robotics, and many effective solutions have been proposed. In this context, Monte Carlo Localization (MCL) is one of the most popular approaches, and represents a good tradeoff between robustness and accuracy. The basic underlying principle of this family of approaches is using a Particle Filter for tracking a probability distribution of the possible robot poses.Whereas the general particle filter framework specifies the sequence of operations that should be performed, it leaves open several choices including the observation and the motion model and it does not directly address the problem of robot kidnapping.The goal of this paper is to provide a systematic analysis of Particle Filter Localization methods, considering the different observation models which can be used in the RoboCup soccer environments. Moreover, we investigate the use of two different particle filtering strategies: the well known Sample Importance Resampling (SIR) filter, and the Auxiliary Variable Particle filter (APF).