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Automatic Registration of Optical Aerial Imagery to a LiDAR Point Cloud for Generation of Large Scale City Models

Abayowa, Bernard Olushola
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1372508452

Abstract Details

2013, Doctor of Philosophy (Ph.D.), University of Dayton, Electrical Engineering.
Textured models of large-scale urban environments are desired in many applications involving scene visualization, analysis and understanding. Aerial Light Detection and Ranging (LiDAR) and optical aerial imagery have complimentary characteristics, which makes them together an efficient data source for generating such models. The aerial LiDAR and optical aerial imagery have to be transformed to a common reference frame before a model can be generated from both data. This dissertation presents a novel framework for automatic registration of both the optical and 3D structural information extracted from oblique aerial imagery to a LiDAR point cloud without prior knowledge of an initial alignment. The framework employs a coarse to fine strategy in the estimation of the registration parameters. First, a dense 3D point cloud and the associated relative camera parameters are extracted from the optical aerial imagery using a state-of-the-art 3D reconstruction algorithm. Next, a digital surface model (DSM) is generated from both the LiDAR and the optical imagery-derived point clouds. Coarse registration parameters are then computed using geometric invariants of salient regional features extracted from the LiDAR and optical imagery-derived DSMs. The registration parameters are further refined using the iterative closest point (ICP) algorithm to minimize global error between the registered point clouds. The registration framework is tested on a simulated scene and aerial datasets acquired in real urban environments. Results demonstrates the robustness of the framework for registering optical and 3D structural information extracted from aerial imagery to a LiDAR point cloud, when co-existing initial registration parameters are unavailable.
Russell Hardie, Ph.D. (Advisor)
Alper Yilmaz, Ph.D. (Advisor)
108 p.
Computer Science; Electrical Engineering
large scale scene modeling; urban 3d reconstruction; 3d registration; aerial imagery; aerial LiDAR

Recommended Citations

Citations

  • Abayowa, B. O. (2013). Automatic Registration of Optical Aerial Imagery to a LiDAR Point Cloud for Generation of Large Scale City Models [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1372508452

    APA Style (7th edition)

  • Abayowa, Bernard. Automatic Registration of Optical Aerial Imagery to a LiDAR Point Cloud for Generation of Large Scale City Models. 2013. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1372508452.

    MLA Style (8th edition)

  • Abayowa, Bernard. "Automatic Registration of Optical Aerial Imagery to a LiDAR Point Cloud for Generation of Large Scale City Models." Doctoral dissertation, University of Dayton, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1372508452

    Chicago Manual of Style (17th edition)

dayton1372508452
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© 2013, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.