A Spanning Tree-Based Multi-Resolution Approach for Pose-Graph Optimization

This paper proposes a computationally efficient method for pose-graph optimization that makes use of a multi-resolution representation of pose-graph transformation constructed on a spanning tree. It is shown that the proposed spanning tree-based hierarchy has a number of advantages over the previously known serial chain-based hierarchy in terms of preservation of sparsity and compatibility with parallel computation. It is demonstrated in numerical experiments using several public datasets that the proposed method outperforms a state-of-the-art solver for large-scale datasets.

Automated summary

This paper presents a computationally efficient method for pose-graph optimization using a multi-resolution representation constructed on a spanning tree. It demonstrates the advantages of the proposed spanning tree-based hierarchy over the previously known serial chain-based hierarchy in terms of sparsity preservation and compatibility with parallel computation. Numerical experiments show that the proposed method outperforms a state-of-the-art solver for large-scale datasets.