Cooperative Localization and Mapping with Robotic Swarms

The Cooperative Localization (CL) problem considers the case where groups of robots aim to improve their overall localization by sharing position estimates within the team instead of using landmarks in the environment. Despite being a well-studied problem, very few works deal with the increased complexity when a very large number of robots is used, as is the case in robotic swarms. In this paper, we propose a methodology to perform cooperative localization in robotic swarms while they navigate through the environment. A collective motion strategy maintains the group’s cohesion, which allows each robot to perform the localization using information from its immediate neighbors. The method is based on the Covariance Intersection (CI) algorithm, which is employed to perform the localization in a decentralized way. Experiments in both simulation and real-world scenarios show the feasibility of the proposed approach. Furthermore, it overcomes and has a reduced space usage and time complexity compared to a traditional centralized EKF-based method. We also investigate how the methodology can be used by a robotic swarm to build occupancy grid maps, a task that generally requires more sophisticated robots and is heavily dependent on good localization estimates.