Scale estimation for monocular SLAM using depth from defocus

Shiozaki, Tomoyuki

Scale estimation for monocular SLAM using depth from defocus

Shiozaki, Tomoyuki

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Publication Type:: Thesis
Issue Date:: 2018

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Field	Value	Language
dc.contributor.author	Shiozaki, Tomoyuki
dc.date.accessioned	2019-01-10T04:05:11Z
dc.date.available	2019-01-10T04:05:11Z
dc.date.issued	2018
dc.identifier.uri	http://hdl.handle.net/10453/129437
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	An autonomous robot must map its environment and estimate its egomotion to perform effectively. Monocular simultaneous localization and mapping (SLAM) can generate maps of the robot’s environment, except for the absolute scale. Alternatives based on stereo or RGB-D camera based SLAM systems can obtain the metric scale but have disadvantages in terms of the cost, size and power requirements. This thesis is focused on the development of an absolute metric scale monocular SLAM system for autonomous robots. A depth from defocus (DfD) technique that relies on image blur is used to estimate the metric scale. However, existing methods for DfD suffer from ambiguities caused by texture, motion blur, and the location of the focal plane. The novelty of this research is combining DfD with camera motion to resolve estimation errors caused by these ambiguities and compute a reliable measure of metric scale. Monocular SLAM algorithms are also prone to scale drift, where the scale gradually changes while mapping. It is demonstrated that integrating DfD into monocular SLAM eliminates scale drift and results in accurate metric scale maps.	en_AU
dc.format	Thesis (ME)
dc.language.iso	en_AU	en_AU
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/129437/2/02whole.pdf
dc.rights	info:eu-repo/semantics/openAccess
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.subject	autonomous robot
dc.subject	simultaneous localization and mapping
dc.subject	SLAM
dc.subject	RGB-D camera
dc.subject	depth from defocus
dc.subject	DfD technique
dc.title	Scale estimation for monocular SLAM using depth from defocus	en_AU
dc.type	Thesis	en_AU
utslib.copyright.status	open_access

Abstract:

An autonomous robot must map its environment and estimate its egomotion to perform effectively. Monocular simultaneous localization and mapping (SLAM) can generate maps of the robot’s environment, except for the absolute scale. Alternatives based on stereo or RGB-D camera based SLAM systems can obtain the metric scale but have disadvantages in terms of the cost, size and power requirements. This thesis is focused on the development of an absolute metric scale monocular SLAM system for autonomous robots. A depth from defocus (DfD) technique that relies on image blur is used to estimate the metric scale. However, existing methods for DfD suffer from ambiguities caused by texture, motion blur, and the location of the focal plane. The novelty of this research is combining DfD with camera motion to resolve estimation errors caused by these ambiguities and compute a reliable measure of metric scale. Monocular SLAM algorithms are also prone to scale drift, where the scale gradually changes while mapping. It is demonstrated that integrating DfD into monocular SLAM eliminates scale drift and results in accurate metric scale maps.

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http://hdl.handle.net/10453/129437