Robust loop closures for multi-robot SLAM in unstructured environments

Abstract

A key capability for a team of robots operating together in an unknown environment is building and sharing maps. As each robot explores, it must be able to build its own local map and use it for navigation. To take advantage of the benefits of working in a team, the robots should also be able to share and merge those maps. Merging these local maps into a global map requires identification of loop closures, or places where the maps overlap. However, tasks in unstructured environments, such as planetary exploration, are not well-suited to traditional visual loop closure methods like scene or object detection. These tasks may involve robots with unusual sensors, the robots may not observe the same areas, and the environment may not allow for identification of standard visual features, which all make it challenging to identify loop closures. The team may also be heterogeneous, so there may be differences in how and where the robots make their observations.This thesis addresses the challenge of identifying robust loop closures in spite of these limitations. It includes several methods that successfully find inter-robot loop closures in challenging unstructured environments, including a method using heterogeneous sensors, a method for robots that view the world from different perspectives, and a method with ranging sensors for scenarios where robots? trajectories do not overlap. It also discusses the Autonomous PUFFER multi-robot SLAM system, a semi-real time system developed for a team of robots operating autonomously in a planetary exploration environment. Finally, it discusses how these techniques provide a framework for future multi-robot mapping in unstructured environments. The maps and systems developed will need to accurately model the environment while also supporting diverse robots and teams.