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The International Journal of Robotics Research
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Experimental Analysis of Sample-Based Maps for Long-Term SLAM

Peter Biber

Deptartment of Computer Science, WSI-GRIS University of Tübingen Tübingen, Germany dr.peter.biber{at}googleemail.com

Tom Duckett

Deptartment of Computing and Informatics University of Lincoln Lincoln LN6 7TS, UK tduckett{at}lincoln.ac.uk

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.

Key Words: simultaneous localization and mapping • dynamic environments • mobile robot navigation • lifelong learning • multi-timescale representations

The International Journal of Robotics Research, Vol. 28, No. 1, 20-33 (2009)
DOI: 10.1177/0278364908096286
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