Multi-Agent Exploration With Similarity Score Map and Topological Memory

Abstract

Multi-robot exploration can be a collaborative solution for navigating a large-scale area. However, it is not trivial to optimally assign tasks among agents because the state dynamically changes while the local observations of multiple agents concurrently update the global map. Furthermore, the individual robots may not have access to accurate relative poses of others or global layouts. We propose an efficient spatial abstraction for multi-agent exploration based on topological graph memories. Each agent creates a topological graph, a lightweight spatial representation whose nodes contain minimal image features. The information in graphs is aggregated to compare individual nodes and is used to update the similarity scores in real-time. Then, the agents effectively fulfill distributed task goals by examining the dynamic similarity scores of frontier nodes. We further exploit extracted visual features to refine the relative poses among topological graphs. Our proposed pipeline can efficiently explore large-scale areas among various scene and robot configurations without sharing precise geometric information.