Computer Science > Robotics
arXiv:2310.06765 (cs)
[Submitted on 10 Oct 2023]
Title:Efficient Graduated Non-Convexity for Pose Graph Optimization
View a PDF of the paper titled Efficient Graduated Non-Convexity for Pose Graph Optimization, by Wonseok Kang and 4 other authors
View PDFAbstract:We propose a novel approach to Graduated Non-Convexity (GNC) and demonstrate its efficacy through its application in robust pose graph optimization, a key component in SLAM backends. Traditional GNC methods often rely on heuristic methods for GNC schedule, updating control parameter {\mu} for escalating the non-convexity. In contrast, our approach leverages the properties of convex functions and convex optimization to identify the boundary points beyond which convexity is no longer guaranteed, thereby eliminating redundant optimization steps in existing methodologies and enhancing both speed and robustness. We show that our method outperforms the state-of-the-art method in terms of speed and accuracy when used for robust back-end pose graph optimization via GNC. Our work builds upon and enhances the open-source riSAM framework. Our implementation can be accessed from:this https URL
| Comments: | 6 pages, 6 figures |
| Subjects: | Robotics (cs.RO) |
| Cite as: | arXiv:2310.06765 [cs.RO] |
| (orarXiv:2310.06765v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2310.06765 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Efficient Graduated Non-Convexity for Pose Graph Optimization, by Wonseok Kang and 4 other authors
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