Abstract
The pose graph is a central data structure in graph-based SLAM approaches. It encodes the poses of the robot during data acquisition as well as spatial constraints between them. The size of the pose graph has a direct influence on the runtime and the memory requirements of a SLAM system since it is typically used to make data associations and within the optimization procedure. In this paper, we address the problem of efficient, information-theoretic compression of such pose graphs. The central question is which sensor measurements can be removed from the graph without loosing too much information. Our approach estimates the expected information gain of laser measurements with respect to the resulting occupancy grid map. It allows us to restrict the size of the pose graph depending on the information that the robot acquires about the environment. Alternatively, we can enforce a maximum number of laser scans the robot is allowed to store, which results in an any-space SLAM system. Real world experiments suggest that our approach efficiently reduces the growth of the pose graph while minimizing the loss of information in the resulting grid map.
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Acknowledgement
This work has partly been supported by the DFG under SFB/TR-8, by the EC under FP7-231888-EUROPA and FP7-260026-TAPAS, and by Microsoft Research, Redmond. We would like to thank Giorgio Grisetti for his contribution to the marginalization as well as Maximilian Beinhofer for fruitful discussions. Furthermore, thanks to Dirk Hähnel for providing the FHW and the Intel Research Lab datasets.
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Stachniss, C., Kretzschmar, H. (2017). Pose Graph Compression for Laser-Based SLAM. In: Christensen, H., Khatib, O. (eds) Robotics Research . Springer Tracts in Advanced Robotics, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-319-29363-9_16
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DOI: https://doi.org/10.1007/978-3-319-29363-9_16
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