Experiences with an interactive museum tour-guide robot
Artificial Intelligence - Special issue on applications of artificial intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Mobile robot mapping and localization in non-static environments
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Learning hierarchical object maps of non-stationary environments with mobile robots
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Spatial learning for navigation in dynamic environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust mobile robot localization in highly non-static environments
Autonomous Robots
Persistent Navigation and Mapping using a Biologically Inspired SLAM System
International Journal of Robotics Research
Long-term mapping and localization using feature stability histograms
Robotics and Autonomous Systems
Localization and navigation of the CoBots over long-term deployments
International Journal of Robotics Research
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This paper presents a system for long-term SLAM (simultaneous localization and mapping) by mobile service robots and its experimental evaluation in a real dynamic environment. To deal with the stability-plasticity dilemma (the trade-off between adaptation to new patterns and preservation of old patterns), the environment is represented by multiple timescales simultaneously (five in our experiments). A sample-based representation is proposed, where older memories fade at different rates depending on the timescale and robust statistics are used to interpret the samples. The dynamics of this representation are analyzed in a five-week experiment, measuring the relative influence of short- and long-term memories over time and further demonstrating the robustness of the approach.