Adaptive Graduated Non-Convexity for Pose Graph Optimization

Abstract

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

4 pages, 3 figures. Accepted for the workshop on Robotic Perception and Mapping(ROPEM): Frontier Vision & Learning Techniques, organized at the 2023 International Conference on Intelligent Robots and Systems (IROS)