Efficient Graduated Non-Convexity for Pose Graph Optimization

TL;DR

This paper introduces an improved Graduated Non-Convexity method for pose graph optimization in SLAM, which enhances speed and robustness by precisely identifying convexity boundaries, outperforming existing approaches.

Contribution

The proposed approach leverages convex function properties to eliminate redundant steps in GNC, significantly improving efficiency and robustness in pose graph optimization.

Findings

Outperforms state-of-the-art in speed and accuracy

Reduces optimization steps by identifying convexity boundaries

Enhances robustness in SLAM back-end applications

Abstract

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