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John W. Fisher, III. Mastin, Dana Andrew Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2010-05-25T20:50:45Z 2010-05-25T20:50:45Z 2009 2009 http://hdl.handle.net/1721.1/55123 593760476 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. Cataloged from PDF version of thesis. Includes bibliographical references (p. 121-123). 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 a camera pose estimation problem. We propose a novel application of mutual information registration which exploits statistical dependencies in urban scenes, using variables such as LIDAR elevation, LIDAR probability of detection (pdet), and optical luminance. We employ the well known downhill simplex optimization to infer camera pose parameters. Utilization of OpenGL and graphics hardware in the optimization process yields registration times on the order of seconds. Using an initial registration comparable to GPS/INS accuracy, we demonstrate the utility of our algorithms with a collection of urban images. Our analysis begins with three basic methods for measuring mutual information. We demonstrate the utility of the mutual information measures with a series of probing experiments and registration tests. We improve the basic algorithms with a novel application of foliage detection, where the use of only non-foliage points improves registration reliability significantly. Finally, we show how the use of an existing registered optical image can be used in conjunction with foliage detection to achieve even more reliable registration. by Dana Andrew Mastin. S.M. 123 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Statistical methods for 2D-3D registration of optical and LIDAR images Statistical methods for two dimensional - three dimensional registration of optical and LIDAR images Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="251dc431-9746-48da-9b24-a587832dcb48"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Statistical methods for 2D-3D registration of optical and LIDAR images</Title> <Subtitle>Statistical methods for two dimensional - three dimensional registration of optical and LIDAR images</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2009</PublicationDate> <Authors> <Author> <DisplayName>Mastin, Dana Andrew</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>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 a camera pose estimation problem. We propose a novel application of mutual information registration which exploits statistical dependencies in urban scenes, using variables such as LIDAR elevation, LIDAR probability of detection (pdet), and optical luminance. We employ the well known downhill simplex optimization to infer camera pose parameters. Utilization of OpenGL and graphics hardware in the optimization process yields registration times on the order of seconds. Using an initial registration comparable to GPS/INS accuracy, we demonstrate the utility of our algorithms with a collection of urban images. Our analysis begins with three basic methods for measuring mutual information. We demonstrate the utility of the mutual information measures with a series of probing experiments and registration tests. We improve the basic algorithms with a novel application of foliage detection, where the use of only non-foliage points improves registration reliability significantly. Finally, we show how the use of an existing registered optical image can be used in conjunction with foliage detection to achieve even more reliable registration.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>