Graph-based SLAM-Aware Exploration with Prior Topo-Metric Information

TL;DR

This paper introduces a SLAM-aware exploration method that leverages prior topo-metric graph information to improve mapping accuracy and exploration efficiency, with a novel path planning algorithm and hierarchical framework.

Contribution

It proposes a new path planning approach that integrates prior topological information to enhance SLAM stability and exploration performance.

Findings

Achieves higher mapping accuracy in complex environments.

Maintains exploration efficiency comparable to existing methods.

Effectively stabilizes SLAM pose graph with globally informative loop-closing actions.

Abstract

Autonomous exploration requires a robot to explore an unknown environment while constructing an accurate map using Simultaneous Localization and Mapping (SLAM) techniques. Without prior information, the exploration performance is usually conservative due to the limited planning horizon. This paper exploits prior information about the environment, represented as a topo-metric graph, to benefit both the exploration efficiency and the pose graph reliability in SLAM. Based on the relationship between pose graph reliability and graph topology, we formulate a SLAM-aware path planning problem over the prior graph, which finds a fast exploration path enhanced with the globally informative loop-closing actions to stabilize the SLAM pose graph. A greedy algorithm is proposed to solve the problem, where theoretical thresholds are derived to significantly prune non-optimal loop-closing actions, without affecting the potential informative ones. Furthermore, we incorporate the proposed planner into a hierarchical exploration framework, with flexible features including path replanning, and online prior graph update that adds additional information to the prior graph. Simulation and real-world experiments indicate that the proposed method can reliably achieve higher mapping accuracy than compared methods when exploring environments with rich topologies, while maintaining comparable exploration efficiency. Our method has been open-sourced on GitHub.