Accurate 3D reconstruction of underwater infrastructure using stereo vision

Leighton, Brenton Lee

Accurate 3D reconstruction of underwater infrastructure using stereo vision

Leighton, Brenton Lee

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Publication Type:: Thesis
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	Leighton, Brenton Lee
dc.date.accessioned	2019-09-18T04:07:10Z
dc.date.available	2019-09-18T04:07:10Z
dc.date.issued	2019
dc.identifier.uri	http://hdl.handle.net/10453/135990
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	Modern vehicle and pedestrian bridges over water are built on concrete piles; foundations that penetrate the soft soil of the bed of the body of water to sit on the solid rock below. Like all built structures these concrete piles require regular inspection to determine if any preventative maintenance is needed. Stereo vision and Structure from Motion techniques offer a cost effective method of creating a 3D reconstructions of a scene, but the underwater environment around a bridge pile has unique challenges. Poor visibility, strong and varying sunlight, and floating material in the water create difficulties for computer vision. This thesis evaluates exposure control, image enhancement, and feature detection and description algorithms, for the purpose of localising images captured around a bridge pile. Stereo correspondence algorithms are evaluated and used to create a single viewpoint 3D reconstruction of a scene, then a visual SLAM system is used to localise the single viewpoint reconstructions, so that they can be merged together to create a 3D reconstruction of a bridge pile. Visual odometry, using KAZE with CLAHE image enhancement for feature detection, was successfully performed in the underwater environment. ORB-SLAM2 can also perform well, and 3D reconstructions from a single viewpoint (created with block matching, semi-global matching, or ELAS) were merged to create 3D reconstructions of submerged bridge piles.	en_AU
dc.format	Thesis (ME)
dc.language.iso	en_AU	en_AU
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/135990/2/02whole.pdf
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Accurate 3D reconstruction of underwater infrastructure using stereo vision	en_AU
dc.type	Thesis	en_AU
utslib.copyright.status	open_access

Abstract:

Modern vehicle and pedestrian bridges over water are built on concrete piles; foundations that penetrate the soft soil of the bed of the body of water to sit on the solid rock below. Like all built structures these concrete piles require regular inspection to determine if any preventative maintenance is needed. Stereo vision and Structure from Motion techniques offer a cost effective method of creating a 3D reconstructions of a scene, but the underwater environment around a bridge pile has unique challenges. Poor visibility, strong and varying sunlight, and floating material in the water create difficulties for computer vision. This thesis evaluates exposure control, image enhancement, and feature detection and description algorithms, for the purpose of localising images captured around a bridge pile. Stereo correspondence algorithms are evaluated and used to create a single viewpoint 3D reconstruction of a scene, then a visual SLAM system is used to localise the single viewpoint reconstructions, so that they can be merged together to create a 3D reconstruction of a bridge pile. Visual odometry, using KAZE with CLAHE image enhancement for feature detection, was successfully performed in the underwater environment. ORB-SLAM2 can also perform well, and 3D reconstructions from a single viewpoint (created with block matching, semi-global matching, or ELAS) were merged to create 3D reconstructions of submerged bridge piles.

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http://hdl.handle.net/10453/135990