Spectral Measurement Sparsification for Pose-Graph SLAM
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2203.13897.pdf
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4.86 MB
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Adobe PDF
Checksum (MD5)
3071850f7d347e40964b86a6a5192d86
Author(s)
Doherty, Kevin J.
Rosen, David M.
Leonard, John J.
Date Issued
October 23, 2022
Publisher
IEEE
Citation
Doherty, Kevin J., Rosen, David M. and Leonard, John J. 2022. "Spectral Measurement Sparsification for Pose-Graph SLAM."
Version
Author's final manuscript
Abstract
Simultaneous localization and mapping (SLAM) is a critical capability in autonomous navigation, but in order to scale SLAM to the setting of "lifelong" SLAM, particularly under memory or computation constraints, a robot must be able to determine what information should be retained and what can safely be forgotten. In graph-based SLAM, the number of edges (measurements) in a pose graph determines both the memory requirements of storing a robot's observations and the computational expense of algorithms deployed for performing state estimation using those observations; both of which can grow unbounded during long-term navigation. To address this, we propose a spectral approach for pose graph sparsification which maximizes the algebraic connectivity of the sparsified measurement graphs, a key quantity which has been shown to control the estimation error of pose graph SLAM solutions. Our algorithm, MAC (for "maximizing algebraic connectivity"), which is based on convex relaxation, is simple and computationally inexpensive, and admits formal post hoc performance guarantees on the quality of the solutions it provides. In experiments on benchmark pose-graph SLAM datasets, we show that our approach quickly produces high-quality sparsification results which retain the connectivity of the graph and, in turn, the quality of corresponding SLAM solutions, as compared to a baseline approach which does not consider graph connectivity.
Description
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) October 23-27, 2022, Kyoto, Japan
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros47612.2022.9981584
