Integration of Airborne Laser Scanner and Optical Data Using Image Tie Points and the Quasi-Log-Polar Analytical Fourier Mellin Transform

LiDAR and camera have complementary data characteristics. Their combined usage allows for achieving better accuracy and enhanced inference about the environment than through the isolated use of the data of one sensor only. Co-registration is the process of spatial alignment of data from different types of sensors. It enables cross-aided sensor- and data calibration and the combined usage of the data. Automatic co-registration of airborne optical imagery and LiDAR data from large-area surveys has not been addressed, unless the datasets come with metadata or some prior knowledge about the scenery or have been pre-processed and converted into data with similar density and structure.

In this dissertation, an innovative co-registration concept that uses image tie points, projected into object space, and Lidar points, is proposed and researched. Since 3D tie points are generated automatically with centimeter-level precision within the photogrammetric block adjustment, this approach has the potential of providing high accuracy and full automation. However, it must address that large area photogrammetric tie points provide extremely low point density compared to laser point clouds, and they describe different aspects of the scanned surface, since tie points coincide with radiometric corner locations on the visual surface whereas laser points locate anywhere on surface objects and on the ground.

This research provides the following contributions: Firstly, the use of 3D photogrammetric tie points for co-registration of large-format sensor data is proposed and analyzed. Secondly, new methods for coarse- and fine registration in space domain are presented and evaluated. Thirdly, five methods for co-registration in frequency domain are researched. Lastly, the novel Quasi Log-Polar Fourier Mellin Transform (QAFMT) method is suggested as tool for automatic coarse co-registration of large area datasets. The learnings and potential use for other applications are discussed in the end. The performance of the QAFMT and its competing methods is analyzed using one simulated and two real-world datasets. While existing algorithms estimated 45 of 144 co-registration parameter pairs correctly (31% success), the QAFMT yielded a 7% improvement (50/144 accurate pairs, 38% success).