Improved two-block coordinate descent method for Pose Graph Optimization Problem under $F^*$-norm
Yongjun Chen, Liping Zhang

TL;DR
This paper improves a coordinate descent method for pose graph optimization in SLAM by reformulating under the F*-norm, resulting in higher accuracy and efficiency, especially in low-observation scenarios.
Contribution
The paper introduces an enhanced two-block coordinate descent method for PGO using the F*-norm, providing explicit solutions, improved initialization, and convergence strategies.
Findings
F*-norm reformulation improves accuracy over F-norm.
The method achieves faster convergence and higher success rates.
Superior performance in low-observation settings.
Abstract
Dual quaternions and dual quaternion matrices are widely used in robotics research, particularly in simultaneous localization and mapping (SLAM) problem. Using dual quaternion theory and graph-based methods, SLAM can be reformulated as a rank-one dual quaternion Hermitian matrix completion problem, known as the pose graph optimization (PGO) problem. Recently, Qi and Cui introduced a two-block coordinate descent method to solve this reformulated problem. In this paper, we enhance this method by reformulating the PGO problem under the more appropriate and robust F*-norm rather than the conventional Frobenius norm, leading to improved experimental accuracy. We show that under the F*-norm, one block has a closed-form solution and another is the optimal rank-one approximation of dual quaternion Hermitian matrices under the F*-norm. We derive an explicit solution for this approximation and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGraph Theory and Algorithms
