ポーズグラフ最適化のための効率的な段階的非凸化
Efficient Graduated Non-Convexity for Pose Graph Optimization
凸関数の性質を利用して非凸化スケジュールの冗長な最適化ステップを削減し、SLAMバックエンドのロバストポーズグラフ最適化を高速かつ高精度化する手法を提案した。
著者: Wonseok Kang, Jaehyun Kim, Jiseong Chung, Seungwon Choi, Tae-wan Kim
分類: cs.RO
原文アブストラクト
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: https://github.com/SNU-DLLAB/EGNC-PGO
