Point Cloud Registration Refinement in an Urban Environment using 2D Edge-Maps

As 3D point cloud acquisition sensors become increasingly prevalent in urban environments (e.g., LiDAR sensors for autonomous vehicles), the need arises to find efficient ways to align large amounts of such 3D data, often in real-time. In this work, we propose a novel method for 3D point cloud registration refinement in an urban environment (e.g., between Terrestrial LiDAR Scans - TLS - and Airborne LiDAR Scans - ALS), assuming an initial coarse registration is available. The proposed method is based on estimation of the direction of gravity, wall detection, projection of the point clouds on a perpendicular horizontal plane, and conversion into 2D edge-maps. Then, two methods are considered for alignment between the 2D edge-maps: a 2D variant of the well-known ICP (Iterative Closest Point) algorithm, and Edge-Map Phase-Correlation (EMPC). We demonstrate the usefulness of the proposed methods for registration in this challenging task, where the 2D variant of ICP achieves a meaningful advantage over 3D ICP in terms of runtime, while maintaining comparable registration accuracy.