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Feature Extraction Based Iterative Closest Point Registration for Large Scale Aerial LiDAR Point Clouds

Graehling, Quinn R
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1607380713807017

Abstract Details

2020, Master of Science in Computer Engineering, University of Dayton, Electrical and Computer Engineering.
Image registration is a major field within computer vision and is often a required step in properly fulfilling other computer vision and pattern recognition tasks such as change detection, scene classification and image segmentation. Recent advances in 3D computer vision and lowered costs in Light Detection and Ranging devices, better known as LiDAR, have given way to an increase in readily available 3D image datasets. These 3D captures give an extra dimension to computer vision data and allow for improvements in a multitude of tasks when compared to their 2D counterparts. However, due to the large scale and complex nature of 3D point cloud data, classical methods for registration often require increased hardware usage and time and can fail to proper register data with a low degree of error. The strategy presented in this paper aims to reduce the number of points representing a point cloud in order to reduce time and hardware overhead needed to perform registration while allowing the algorithm to improve registration accuracy and reduce error between registered clouds. This is done by extracting key edge features from the point clouds using eigenvector analysis to remove ground planes and large normal planes within the point cloud. The algorithm is further improved by performing set differencing on two separate edge extractions to remove large clusters of points representing natural objects that can often cause confusion for registration of outdoor LiDAR scenes. The method for key point registration is evaluated on large scale, complex LiDAR point clouds obtained from aerial sensors. Tests are performed on both fully overlapping and partially overlapping clouds to ensure that the method increases performance on full and partial registration tasks. The tests are also performed on clouds of varying resolution to test the algorithms ability to maintain integrity regardless of cloud resolution. Point reduction results, registration statics and visual results are presented for comparison. A brief look into possible applications of the method and future improvements to the algorithm are included.
Vijayan Asari (Committee Chair)
Theus Aspiras (Committee Member)
Eric Balster (Committee Chair)
42 p.
Artificial Intelligence; Computer Engineering; Computer Science; Electrical Engineering; Engineering
Registration; Point Clouds; LiDAR; Iterative Closest Point; 3D Computer Vision

Recommended Citations

Citations

  • Graehling, Q. R. (2020). Feature Extraction Based Iterative Closest Point Registration for Large Scale Aerial LiDAR Point Clouds [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1607380713807017

    APA Style (7th edition)

  • Graehling, Quinn. Feature Extraction Based Iterative Closest Point Registration for Large Scale Aerial LiDAR Point Clouds. 2020. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1607380713807017.

    MLA Style (8th edition)

  • Graehling, Quinn. "Feature Extraction Based Iterative Closest Point Registration for Large Scale Aerial LiDAR Point Clouds." Master's thesis, University of Dayton, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1607380713807017

    Chicago Manual of Style (17th edition)

dayton1607380713807017
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© 2020, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.