Cooperative localization has been proved to effectively outperform single-robot localization. While most of the state-of-the-art multi-robot localization systems either treat moving objects as outliers or accomplish moving object tracking separately from localization, we argue that augmenting moving objects into the localization estimation can further enhance localization performance and is indeed the key to solve several localization challenges such as insufficient map features, no map features, and symmetric maps. In this paper, a multi-robot simultaneous localization and tracking (MR-SLAT) algorithm based on the extended Kalman filter is proposed, and multiple hypothesis tracking (MHT) is integrated into MR-SLAT for dealing with challenging data association issues.
The proposed approach is verified in two scenarios: the NAO humanoid robots equipped with cameras and WiFi are used in the RoboCup scenario and the robotic vehicles with laser scanners and dedicated short-range communications (DSRC) are used in the traffic scenario. The experiments with ground truth show that MR-SLAT, by exploiting moving objects, is superior to single-robot localization and cooperative localization in challenging scenarios. Ample experimental and simulation results demonstrate the effectiveness of exploiting moving objects and the generality and feasibility of the proposed MR-SLAT algorithm.