Automatic registration of LIDAR and optical images of urban scenes
Author(s)Mastin, Dana Andrew; Kepner, Jeremy; Fisher, John W., III
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Fusion of 3D laser radar (LIDAR) imagery and aerial optical imagery is an efficient method for constructing 3D virtual reality models. One difficult aspect of creating such models is registering the optical image with the LIDAR point cloud, which is characterized as a camera pose estimation problem. We propose a novel application of mutual information registration methods, which exploits the statistical dependency in urban scenes of optical appearance with measured LIDAR elevation. We utilize the well known downhill simplex optimization to infer camera pose parameters. We discuss three methods for measuring mutual information between LIDAR imagery and optical imagery. Utilization of OpenGL and graphics hardware in the optimization process yields registration times dramatically lower than previous methods. Using an initial registration comparable to GPS/INS accuracy, we demonstrate the utility of our algorithm with a collection of urban images and present 3D models created with the fused imagery.
DepartmentLincoln Laboratory; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009.
Institute of Electrical and Electronics Engineers
Mastin, A., J. Kepner, and J. Fisher. “Automatic registration of LIDAR and optical images of urban scenes.” Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. 2009. 2639-2646. © Copyright 2010 IEEE
Final published version
INSPEC Accession Number: 10836100