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Spherical mobile mapping system proposes two ground normal vector estimation methods
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Tim Schubert, Fabian Arzberger, and Andreas Nüchter propose two RANSAC-based ground normal vector estimation methods—single-scan estimation and estimation based on a LiDAR-inertial odometry (LIO) global 3D map—for odometry in a spherical mobile mapping system (SMMS). Evaluated on diverse real-world datasets against ground truth from a terrestrial laser scanner, the results show that the LIO method significantly outperforms the single-scan method in accuracy and robustness; incorporating ground normal vector estimation into the motion model improves pose estimation and supports autonomous navigation in hazardous environments.
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- Real-Time Ground Normal Vector Estimation for Spherical Mobile 3D Mapping Using LiDAR-Inertial Odometry
The study proposes two RANSAC-based ground normal vector estimation methods: single-scan estimation and estimation based on a LiDAR-inertial odometry (LIO) global 3D map, for odometry in a spherical mobile mapping system (SMMS). Evaluated on diverse real-world datasets against terrestrial laser scanner ground truth, the LIO method significantly outperforms the single-scan method in accuracy and robustness. Integrating ground normal vector estimation into the motion model improves pose estimation and supports autonomous navigation in hazardous environments.
arXiv cs.RO ·