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<p>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 <i>μ</i> for escalating the non-convexity. 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: <a href="https://github.com/SNU-DLLAB/EGNC-PGO">https://github.com/SNU-DLLAB/EGNC-PGO</a>.</p>

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Improving Pose Graph Optimization via Efficient Graduated Non-convexity Scheduling

  • 期刊论文

  • 发表日期：

    2025年2月5日

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  • Wonseok Kang,
  • Jaehyun Kim,
  • Jiseong Chung,
  • Seungwon Choi,
  • Tae-wan Kim

摘要

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 non-convexity. 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.