Comparison of 2d and 3D approaches for the alignment of UAV and lidar point clouds

Abstract

The automatic alignment of 3D point clouds acquired or generated from different sensors is a challenging problem. The objective of the alignment is to estimate the 3D similarity transformation parameters, including a global scale factor, 3 rotations and 3 translations. To do so, corresponding anchor features are required in both data sets. There are two main types of alignment: i) Coarse alignment and ii) Refined Alignment. Coarse alignment issues include lack of any prior knowledge of the respective coordinate systems for a source and target point cloud pair and the difficulty to extract and match corresponding control features (e.g., points, lines or planes) co-located on both point cloud pairs to be aligned. With the increasing use of UAVs, there is a need to automatically co-register their generated point cloud-based digital surface models with those from other data acquisition systems such as terrestrial or airborne lidar point clouds. This works presents a comparative study of two independent feature matching techniques for addressing 3D conformal point cloud alignment of UAV and lidar data in different 3D coordinate systems without any prior knowledge of the seven transformation parameters.