Improved Pose Graph Optimization for Planar Motions Using Riemannian Geometry on the Manifold of Dual Quaternions
This work addresses pose graph optimization for planar motions, which is incremental as it improves robustness in robotics and SLAM applications.
The paper tackled planar pose graph optimization by formulating a cost function using Riemannian geometry on the dual quaternion manifold, resulting in equivalent accuracy and better convergence robustness under large uncertainties compared to state-of-the-art methods.
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.