Automatic registration of LIDAR and optical images of urban scenes
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....
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers
2010
|
| Online Access: | http://hdl.handle.net/1721.1/59839 https://orcid.org/0000-0003-4844-3495 |
| _version_ | 1811090692433248256 |
|---|---|
| author | Mastin, Dana Andrew Kepner, Jeremy Fisher, John W., III |
| author2 | Lincoln Laboratory |
| author_facet | Lincoln Laboratory Mastin, Dana Andrew Kepner, Jeremy Fisher, John W., III |
| author_sort | Mastin, Dana Andrew |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:50:21Z |
| format | Article |
| id | mit-1721.1/59839 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:50:21Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/598392022-10-01T22:50:40Z Automatic registration of LIDAR and optical images of urban scenes Mastin, Dana Andrew Kepner, Jeremy Fisher, John W., III Lincoln Laboratory Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Fisher, John W., III Mastin, Dana Andrew Kepner, Jeremy Fisher, John W., III 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. United States. Air Force Office of Scientific Research (Award No. FA9550-06-1-0324) 2010-11-05T19:01:18Z 2010-11-05T19:01:18Z 2009-08 2009-06 Article http://purl.org/eprint/type/JournalArticle 978-1-4244-3992-8 1063-6919 INSPEC Accession Number: 10836100 http://hdl.handle.net/1721.1/59839 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 https://orcid.org/0000-0003-4844-3495 en_US http://dx.doi.org/10.1109/CVPRW.2009.5206539 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Mastin, Dana Andrew Kepner, Jeremy Fisher, John W., III Automatic registration of LIDAR and optical images of urban scenes |
| title | Automatic registration of LIDAR and optical images of urban scenes |
| title_full | Automatic registration of LIDAR and optical images of urban scenes |
| title_fullStr | Automatic registration of LIDAR and optical images of urban scenes |
| title_full_unstemmed | Automatic registration of LIDAR and optical images of urban scenes |
| title_short | Automatic registration of LIDAR and optical images of urban scenes |
| title_sort | automatic registration of lidar and optical images of urban scenes |
| url | http://hdl.handle.net/1721.1/59839 https://orcid.org/0000-0003-4844-3495 |
| work_keys_str_mv | AT mastindanaandrew automaticregistrationoflidarandopticalimagesofurbanscenes AT kepnerjeremy automaticregistrationoflidarandopticalimagesofurbanscenes AT fisherjohnwiii automaticregistrationoflidarandopticalimagesofurbanscenes |