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We present a novel Riemannian approach for planar pose graph optimizationproblems. By formulating the cost function based on the Riemannian metric onthe manifold of dual quaternions representing planar motions, the nonlinearstructure of the SE(2) group is inherently considered. To solve the on-manifoldleast squares problem, a Riemannian Gauss-Newton method using the exponentialretraction is applied. The proposed Riemannian pose graph optimizer (RPG-Opt)is further evaluated based on public planar pose graph data sets. Compared withstate-of-the-art frameworks, the proposed method gives equivalent accuracy andbetter convergence robustness under large uncertainties of odometrymeasurements.

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