Exploiting Moving Objects: Multi-Robot Simultaneous Localization and Tracking


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 have a tendency to argue that augmenting moving objects into the localization estimation can further enhance localization performance and is indeed the key to resolve several localization challenges like insufficient map options, no map options, and symmetric maps. During 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 handling difficult knowledge association issues. The proposed approach is verified in two eventualities: the NAO humanoid robots equipped with cameras and WiFi are utilized in the RoboCup scenario and also the robotic vehicles with laser scanners and dedicated short-range communications (DSRC) are utilized in the traffic situation. The experiments with ground truth show that MR-SLAT, by exploiting moving objects, is superior to single-robot localization and cooperative localization in difficult eventualities. Ample experimental and simulation results demonstrate the effectiveness of exploiting moving objects and therefore the generality and feasibility of the proposed MR-SLAT algorithm.

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