Mitigating Shadows in Lidar Scan Matching using Spherical Voxels
This addresses systematic errors in Lidar-based localization for robotics or autonomous vehicles, particularly in varied terrains, though it appears incremental as an enhancement to existing scan-matching methods.
The paper tackles shadowing errors in Lidar scan matching by introducing a preprocessing step using spherical gridding, which eliminates shadow edges more effectively than ground-plane removal and works on arbitrary terrains while preserving ground points for height, pitch, and roll estimation.
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.