Bisheng Yang, Chi Chen

Abstract：Use of direct geo-referencing data leads to registration failure between sequent images and LiDAR data captured by mini-UAV platforms because of low-cost sensors. This paper therefore proposes a novel automatic registration method for sequent images and LiDAR data captured by mini-UAVs. First, the proposed method extracts building outlines from LiDAR data and images and estimates the exterior orientation parameters (EoPs) of the images with building objects in the LiDAR data coordinate framework based on corresponding corner points derived indirectly by using linear features. Second, the EoPs of the sequent images in the image coordinate framework are recovered using a structure from motion (SfM) technique, and the transformation matrices between the LiDAR coordinate and image coordinate frameworks are calculated using corresponding EoPs, resulting in a coarse registration between the images and the LiDAR data. Finally, 3D points are generated from sequent images by multi-view stereo (MVS) algorithms. Then the EoPs of the sequent images are further refined by registering the LiDAR data and the 3D points using an iterative closest-point (ICP) algorithm with the initial results from coarse registration, resulting in a fine registration between sequent images and LiDAR data. Experiments were performed to check the validity and effectiveness of the proposed method. The results show that the proposed method achieves high-precision robust co-registration of sequent images and LiDAR data captured by mini-UAVs.

Keywords: Unmanned aerial vehicles mapping；LiDAR data；Sequent images；Registration；Linear features；Multi-view stereo

Download