ポーズグラフ最適化のための適応的段階的非凸性
Adaptive Graduated Non-Convexity for Pose Graph Optimization
Bスプラインでロバストカーネルの形状を適応的に最適化し、GNCの反復回数を削減して精度を保ちつつ計算を高速化する手法を提案した。
著者: Seungwon Choi, Wonseok Kang, Jiseong Chung, Jaehyun Kim, Tae-wan Kim
分類: cs.RO
原文アブストラクト
We present a novel approach to robust pose graph optimization based on Graduated Non-Convexity (GNC). Unlike traditional GNC-based methods, the proposed approach employs an adaptive shape function using B-spline to optimize the shape of the robust kernel. This aims to reduce GNC iterations, boosting computational speed without compromising accuracy. When integrated with the open-source riSAM algorithm, the method demonstrates enhanced efficiency across diverse datasets. Accompanying open-source code aims to encourage further research in this area. https://github.com/SNU-DLLAB/AGNC-PGO
