Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
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
Dynamic motion models in Monte Carlo Localization
Integrated Computer-Aided Engineering
Tackling the premature convergence problem in Monte-Carlo localization
Robotics and Autonomous Systems
Probabilistic multi-component extended strong tracking filter for mobile robot global localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Self-adaptive Monte Carlo localization for mobile robots using range sensors
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
An Ultrasound-based Localization Algorithm for Catheter Ablation Guidance in the Left Atrium
International Journal of Robotics Research
A moving grid cell based MCL algorithm for mobile robot localization
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Dynamic maps in monte carlo localization
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Global localization with non-quantized local image features
Robotics and Autonomous Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multi-observation sensor resetting localization with ambiguous landmarks
Autonomous Robots
Expert Systems with Applications: An International Journal
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Global mobile robot localization is the problem of determining a robot's pose in an environment, using sensor data, when the starting position is unknown. A family of probabilistic algorithms known as Monte Carlo Localization (MCL) is currently among the most popular methods for solving this problem. MCL algorithms represent a robot's belief by a set of weighted samples, which approximate the posterior probability of where the robot is located by using a Bayesian formulation of the localization problem. This article presents an extension to the MCL algorithm, which addresses its problems when localizing in highly symmetrical environments; a situation where MCL is often unable to correctly track equally probable poses for the robot. The problem arises from the fact that sample sets in MCL often become impoverished, when samples are generated according to their posterior likelihood. Our approach incorporates the idea of clusters of samples and modifies the proposal distribution considering the probability mass of those clusters. Experimental results are presented that show that this new extension to the MCL algorithm successfully localizes in symmetric environments where ordinary MCL often fails.