International Journal of Control, Automation, and Systems 2025; 23(2): 405-417
https://doi.org/10.1007/s12555-024-0479-y
© The International Journal of Control, Automation, and Systems
In this study, 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 rely on heuristic methods for GNC schedule, updating control parameter µ for escalating the nonconvexity. However, our approach leverages the properties of convex functions and convex optimization to identify the boundary points beyond which convexity is not guaranteed, thereby eliminating redundant optimization steps in existing methodologies and enhancing both speed and robustness. We demonstrate 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: https://github.com/SNU-DLLAB/EGNC-PGO.
Keywords Convex optimization, graduated non-convexity, outlier handling, pose graph optimization, robust optimization techniques.
International Journal of Control, Automation, and Systems 2025; 23(2): 405-417
Published online February 1, 2025 https://doi.org/10.1007/s12555-024-0479-y
Copyright © The International Journal of Control, Automation, and Systems.
Wonseok Kang, Jaehyun Kim, Jiseong Chung, Seungwon Choi, and Tae-wan Kim*
Seoul National University
In this study, 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 rely on heuristic methods for GNC schedule, updating control parameter µ for escalating the nonconvexity. However, our approach leverages the properties of convex functions and convex optimization to identify the boundary points beyond which convexity is not guaranteed, thereby eliminating redundant optimization steps in existing methodologies and enhancing both speed and robustness. We demonstrate 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: https://github.com/SNU-DLLAB/EGNC-PGO.
Keywords: Convex optimization, graduated non-convexity, outlier handling, pose graph optimization, robust optimization techniques.
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