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Abstract

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.

Keywords

simultaneous localization and mapping dynamic environments mobile robot navigation lifelong learning multi-timescale representations

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Experimental Analysis of Sample-Based Maps for Long-Term SLAM

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