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

I. Introduction

Simultaneous Localization and Mapping (SLAM) has become an essential piece in the perception stack of most robots and autonomous vehicles. SLAM systems typically rely on two sources of information: odometry measurements (which describe the motion of the robot) and loop closures (which occur when a robot revisits an already seen location).