FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning hierarchical object maps of non-stationary environments with mobile robots
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Modeling dynamic scenarios for local sensor-based motion planning
Autonomous Robots
Experimental Analysis of Sample-Based Maps for Long-Term SLAM
International Journal of Robotics Research
Probabilistic mobile manipulation in dynamic environments, with application to opening doors
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
GATMO: a generalized approach to tracking movable objects
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Robust mobile robot localization in highly non-static environments
Autonomous Robots
Lifelong localization in changing environments
International Journal of Robotics Research
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Whenever mobile robots act in the real world, they need to be able to deal with non-static objects. In the context of mapping, a common technique to deal with dynamic objects is to filter out the spurious measurements corresponding to such objects. In this paper, we present a novel approach to estimate typical configurations of dynamic areas in the environment of a mobile robot. Our approach clusters local grid maps to identify the possible configurations. We furthermore describe how these clusters can be utilized within a Rao-Blackwellized particle filter to localize a mobile robot in a non-static environment. In practical experiments carried out with a mobile robot in a typical office environment, we demonstrate the advantages of our approach compared to alternative techniques for mapping and localization in dynamic environments.