A robust approach for automatic registration of aerial images with untextured aerial LiDAR data

Airborne LiDAR technology draws increasing interest in large-scale 3D urban modeling in recent years. 3D LiDAR data typically has no texture information. To generate photo-realistic 3D models, oblique aerial images are needed for texture mapping, in which the key step is to obtain accurate registration between aerial images and untextured 3D LiDAR data. We present a robust automatic registration approach. A novel feature called 3CS is proposed which is composed of connected line segments. Putative line segment correspondences are obtained by matching 3CS features detected from both aerial images and 3D LiDAR data. Outliers are removed with a two-level RANSAC algorithm that integrates local and global processing to improve robustness and efficiency. The approach has been tested on 2290 aerial images that cover a variety of urban environments in Oakland and Atlanta areas. Its correct pose recovery rate is over 98%.