Spectral Measurement Sparsification for Pose-Graph SLAM

TL;DR

This paper introduces MAC, a spectral sparsification method for pose-graph SLAM that efficiently retains graph connectivity and SLAM accuracy during long-term navigation by maximizing algebraic connectivity.

Contribution

The paper presents a novel convex relaxation-based spectral sparsification algorithm, MAC, that improves pose-graph SLAM scalability while providing formal performance guarantees.

Findings

MAC produces high-quality sparsification results quickly.

Retains graph connectivity and SLAM solution quality.

Outperforms baseline methods in experiments.

Abstract

Simultaneous localization and mapping (SLAM) is a critical capability in autonomous navigation, but in order to scale SLAM to the setting of "lifelong" SLAM, particularly under memory or computation constraints, a robot must be able to determine what information should be retained and what can safely be forgotten. In graph-based SLAM, the number of edges (measurements) in a pose graph determines both the memory requirements of storing a robot's observations and the computational expense of algorithms deployed for performing state estimation using those observations; both of which can grow unbounded during long-term navigation. To address this, we propose a spectral approach for pose graph sparsification which maximizes the algebraic connectivity of the sparsified measurement graphs, a key quantity which has been shown to control the estimation error of pose graph SLAM solutions. Our algorithm, MAC (for "maximizing algebraic connectivity"), which is based on convex relaxation, is simple and computationally inexpensive, and admits formal post hoc performance guarantees on the quality of the solutions it provides. In experiments on benchmark pose-graph SLAM datasets, we show that our approach quickly produces high-quality sparsification results which retain the connectivity of the graph and, in turn, the quality of corresponding SLAM solutions, as compared to a baseline approach which does not consider graph connectivity.