Improved Pose Graph Optimization for Planar Motions Using Riemannian Geometry on the Manifold of Dual Quaternions

2019/07/31 by Kailai Li, Li, Kailai, Johannes Cox +5
Computer Science · Engineering · #Advanced Vision and Imaging #FOS: Computer and information sciences #Robotic Mechanisms and Dynamics #Robotics (cs.RO) #Robotics and Sensor-Based Localization

paper · pdf · doi:10.48550/arxiv.1907.13566

openalex publication_date 2019/07/31 · openalex created_date 2019/08/13 · openalex updated_date 2026/07/28

Abstract

We present a novel Riemannian approach for planar pose graph optimization problems. By formulating the cost function based on the Riemannian metric on the manifold of dual quaternions representing planar motions, the nonlinear structure of the SE(2) group is inherently considered. To solve the on-manifold least squares problem, a Riemannian Gauss-Newton method using the exponential retraction is applied. The proposed Riemannian pose graph optimizer (RPG-Opt) is further evaluated based on public planar pose graph data sets. Compared with state-of-the-art frameworks, the proposed method gives equivalent accuracy and better convergence robustness under large uncertainties of odometry measurements.

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