In this work, we present a novel teach-and-repeat (T&R) method for omnidirectional-drive autonomous ground vehicles, based on the tightly-coupled fusion of LiDAR and odometry readings in a relative representation. In addition to robot localization, we perform online estimation of the platform intrinsic parameters, which significantly enhances the robustness and localization accuracy of the system. We demonstrate the effectiveness of our approach, including the performance of the platform parameters and pose estimation, as well as the general T&R method, through simulation.
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Bedín et al. (2023) studied this question.
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