Research on 2D general feature based SLAM algorithm for mobile robot

Zhao, Jiaheng

Research on 2D general feature based SLAM algorithm for mobile robot

Zhao, Jiaheng

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Publication Type:: Thesis
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Zhao, Jiaheng
dc.date.accessioned	2021-11-02T03:07:18Z
dc.date.available	2021-11-02T03:07:18Z
dc.date.issued	2021
dc.identifier.uri	http://hdl.handle.net/10453/151305
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Simultaneous Localization and Mapping (SLAM) is a fundamental research problem for autonomous robot navigation and map construction. This thesis studied the problem of improving the performance of localization and mapping for mobile robots, including pre-fitting features with ellipse representation, representing features with implicit functions, parameterization in Fourier series, and submap joining. The main contributions include three aspects: (1) a SLAM algorithm with pre-fitted conic features via 2D lidar is presented, which is named as Pre-fit SLAM and can be adapted to an open environment nicely; (2) a post-count framework for 2D lidar SLAM with implicit functions on general features is studied; (3) a 2D laser SLAM approach with Fourier series based feature parameterization (called Fourier-SLAM) and submap joining is studied.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/151305/2/02whole.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.title	Research on 2D general feature based SLAM algorithm for mobile robot	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Simultaneous Localization and Mapping (SLAM) is a fundamental research problem for autonomous robot navigation and map construction. This thesis studied the problem of improving the performance of localization and mapping for mobile robots, including pre-fitting features with ellipse representation, representing features with implicit functions, parameterization in Fourier series, and submap joining. The main contributions include three aspects: (1) a SLAM algorithm with pre-fitted conic features via 2D lidar is presented, which is named as Pre-fit SLAM and can be adapted to an open environment nicely; (2) a post-count framework for 2D lidar SLAM with implicit functions on general features is studied; (3) a 2D laser SLAM approach with Fourier series based feature parameterization (called Fourier-SLAM) and submap joining is studied.

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http://hdl.handle.net/10453/151305