Cross-Agent Relocalization for Decentralized Collaborative SLAM

Abstract

State-of-the-art decentralized collaborative Simultaneous Localization And Mapping (SLAM) systems crucially lack the ability to effectively use well-mapped areas generated by other agents in the team for relocalization. This often leads to map redundancy between agents, inefficient communication, and the need for costly re-mapping of areas previously mapped by other agents. In this work, we propose a strategy to efficiently share the areas mapped by different agents in a collaborative, decentralized SLAM system. This approach directly addresses map redundancy while maintaining the consistency of the estimates across the agents and keeping the overall system scalable in terms of cross-agent communication and individual computational effort. Our method leverages covisibility information between keyframes instantiated by different agents to transfer local sub-maps on-the-fly in a completely decentralized, peer-to-peer fashion. A globally consistent estimate is achieved by solving a distributed bundle adjustment problem using the Alternating Direction Method of Multipliers (ADMM), where we enforce constraints on shared map points and keyframes across agents.