KI - Künstliche Intelligenz

, Volume 24, Issue 3, pp 199–206 | Cite as

Lifelong Map Learning for Graph-based SLAM in Static Environments

  • Henrik Kretzschmar
  • Giorgio Grisetti
  • Cyrill Stachniss


In this paper, we address the problem of lifelong map learning in static environments with mobile robots using the graph-based formulation of the simultaneous localization and mapping problem. The pose graph, which stores the poses of the robot and spatial constraints between them, is the central data structure in graph-based SLAM. The size of the pose graph has a direct influence on the runtime and the memory complexity of the SLAM system and typically grows over time. A robot that performs lifelong mapping in a bounded environment has to limit the memory and computational complexity of its mapping system. We present a novel approach to prune the pose graph so that it only grows when the robot acquires relevant new information about the environment in terms of expected information gain. As a result, our approach scales with the size of the environment and not with the length of the trajectory, which is an important prerequisite for lifelong map learning. The experiments presented in this paper illustrate the properties of our method using real robots.


SLAM Mapping Expected information gain 



We would like to thank Dirk Hähnel for providing the Intel Research Lab dataset. This work has partly been supported by the German Research Foundation (DFG) under contract number SFB/TR-8 and by the European Commission under contract number FP7-ICT-231888-EUROPA.


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Henrik Kretzschmar
    • 1
  • Giorgio Grisetti
    • 1
  • Cyrill Stachniss
    • 1
  1. 1.Department of Computer ScienceUniversity of FreiburgFreiburgGermany

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