Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM

Multi-robot SLAM systems in GPS-denied environments require loop closures to maintain a drift-free centralized map. With an increasing number of robots and size the environment, checking computing transformation for all closure candidates becomes computationally infeasible. In this work, we describe module that is able prioritize which compute based on underlying pose graph, proximity known beacons, characteristics point clouds. We validate system context DARPA Subterranean Challenge four challenging underground datasets where demonstrate ability generate map with low error. find our proposed techniques are select effective results 51% mean reduction median error when compared odometric solution 75% baseline version no prioritization. also achieve lower mission time one hour processes every possible half hours.