Visual reconstruction
Simultaneous Localization, Mapping and Moving Object Tracking
International Journal of Robotics Research
Robust and efficient robotic mapping
Robust and efficient robotic mapping
Mobile robot mapping and localization in non-static environments
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Probabilistic mobile manipulation in dynamic environments, with application to opening doors
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
People tracking with anonymous and ID-sensors using Rao-Blackwellised particle filters
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
GATMO: a generalized approach to tracking movable objects
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Learning hierarchical object maps of non-stationary environments with mobile robots
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
Information-theoretic compression of pose graphs for laser-based SLAM
International Journal of Robotics Research
Long-term robot mapping in dynamic environments
Long-term robot mapping in dynamic environments
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Robot localization systems typically assume that the environment is static, ignoring the dynamics inherent in most real-world settings. Corresponding scenarios include households, offices, warehouses and parking lots, where the location of certain objects such as goods, furniture or cars can change over time. These changes typically lead to inconsistent observations with respect to previously learned maps and thus decrease the localization accuracy or even prevent the robot from globally localizing itself. In this paper we present a sound probabilistic approach to lifelong localization in changing environments using a combination of a Rao-Blackwellized particle filter with a hidden Markov model. By exploiting several properties of this model, we obtain a highly efficient map management approach for dynamic environments, which makes it feasible to run our algorithm online. Extensive experiments with a real robot in a dynamically changing environment demonstrate that our algorithm reliably adapts to changes in the environment and also outperforms the popular Monte-Carlo localization approach.