Mitigating Shadows in Lidar Scan Matching using Spherical Voxels
| Autor: | Matthew McDermott, Jason Rife |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Control and Optimization Mechanical Engineering Computer Vision and Pattern Recognition (cs.CV) Biomedical Engineering Computer Science - Computer Vision and Pattern Recognition Computer Science Applications Human-Computer Interaction Computer Science - Robotics Artificial Intelligence Control and Systems Engineering I.4.8 Computer Vision and Pattern Recognition Robotics (cs.RO) |
| DOI: | 10.48550/arxiv.2208.01150 |
| Popis: | In this paper we propose an approach to mitigate shadowing errors in Lidar scan matching, by introducing a preprocessing step based on spherical gridding. Because the grid aligns with the Lidar beam, it is relatively easy to eliminate shadow edges which cause systematic errors in Lidar scan matching. As we show through simulation, our proposed algorithm provides better results than ground-plane removal, the most common existing strategy for shadow mitigation. Unlike ground plane removal, our method applies to arbitrary terrains (e.g. shadows on urban walls, shadows in hilly terrain) while retaining key Lidar points on the ground that are critical for estimating changes in height, pitch, and roll. Our preprocessing algorithm can be used with a range of scan-matching methods; however, for voxel-based scan matching methods, it provides additional benefits by reducing computation costs and more evenly distributing Lidar points among voxels. |
| Databáze: | OpenAIRE |
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