Generalized Proximal Methods for Pose Graph Optimization
This work addresses the problem of accelerating and distributing pose graph optimization for robotics and sensor network applications, offering a faster and more scalable solution.
This paper generalizes proximal methods from convex optimization to non-convex pose graph optimization (PGO) on special Euclidean groups, demonstrating convergence to first-order critical points. The proposed methods are significantly faster than existing techniques for practical accuracy in SLAM and distributed 3D sensor network localization.
In this paper, we generalize proximal methods that were originally designed for convex optimization on normed vector space to non-convex pose graph optimization (PGO) on special Euclidean groups, and show that our proposed generalized proximal methods for PGO converge to first-order critical points. Furthermore, we propose methods that significantly accelerate the rates of convergence almost without loss of any theoretical guarantees. In addition, our proposed methods can be easily distributed and parallelized with no compromise of efficiency. The efficacy of this work is validated through implementation on simultaneous localization and mapping (SLAM) and distributed 3D sensor network localization, which indicate that our proposed methods are a lot faster than existing techniques to converge to sufficient accuracy for practical use.