Date of Award:
12-2025
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Electrical and Computer Engineering
Committee Chair(s)
Scott E. Budge
Committee
Scott E. Budge
Committee
Jonathan Phillips
Committee
Jacob Gunther
Abstract
Drones equipped with laser scanners (LiDAR) and cameras capture detailed 3D scenes. Combining the laser points with photos builds realistic, photo-textured 3D maps used in surveying, agriculture, and infrastructure planning. Conventional methods to create these maps often misrepresent inward shapes such as doorways, overhangs, and outcrops. These shapes are misrepresented because drones mainly view top surfaces and do not collect significant data on vertical or hidden areas.
This study introduces a surface-reconstruction method that groups LiDAR points into clusters, reconstructs smooth surfaces for each cluster, and uses information from recorded camera poses to stitch clusters together. Tests on real drone flights with synchronized LiDAR, imagery, and navigation data show that the method better preserves concave features, mitigates errors from sparse sampling, and improves the completeness and accuracy of the resulting 3D maps.
Checksum
fe51d4394231a625b90a966d9384bbb3
Recommended Citation
Kiguthi, Samuel, "Reconstruction and Texturing of 3D Surfaces From Fused Low-Cost Aerial Lidar and Optical Imagery" (2025). All Graduate Theses and Dissertations, Fall 2023 to Present. 661.
https://digitalcommons.usu.edu/etd2023/661
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