Filter based active SLAM in static and deformable environments

Xu, Mengya

Filter based active SLAM in static and deformable environments

Xu, Mengya

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Publication Type:: Thesis
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Xu, Mengya
dc.date.accessioned	2025-05-26T03:22:57Z
dc.date.available	2025-05-26T03:22:57Z
dc.date.issued	2024
dc.identifier.uri	http://hdl.handle.net/10453/187505
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Active simultaneous localization and mapping (SLAM) is a very important decision-making problem when a robot is navigating in an unknown environment. A good active SLAM algorithm can help improve the estimation accuracy of the map and robot localization, and additionally take into consideration other tasks such as coverage and time of exploration at the same time. This dissertation focuses on the invariant extended Kalman filter (EKF) based method, aiming to solve the active SLAM problem in both static and deformable environments. Firstly, an improved EKF based active SLAM method was designed to explore a feature-based static environment. The Right Invariant Extended Kalman filter (RIEKF) algorithm, which has been shown to be able to get better SLAM results with much improved consistency, was used in our active SLAM algorithm. Afterward, we extended the filter based active SLAM algorithm to the feature-based deformable environments. The algorithm was designed based on possible assumptions of the feature dynamic model. The designed algorithms were tested and evaluated both in simulation and in the real world in terms of estimation accuracy, coverage, and exploration time.	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/10453/187505/1/thesis.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	© 2024 Mengya Xu
dc.rights	au.edu.uts.lib/cph
dc.title	Filter based active SLAM in static and deformable environments	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Active simultaneous localization and mapping (SLAM) is a very important decision-making problem when a robot is navigating in an unknown environment. A good active SLAM algorithm can help improve the estimation accuracy of the map and robot localization, and additionally take into consideration other tasks such as coverage and time of exploration at the same time. This dissertation focuses on the invariant extended Kalman filter (EKF) based method, aiming to solve the active SLAM problem in both static and deformable environments. Firstly, an improved EKF based active SLAM method was designed to explore a feature-based static environment. The Right Invariant Extended Kalman filter (RIEKF) algorithm, which has been shown to be able to get better SLAM results with much improved consistency, was used in our active SLAM algorithm. Afterward, we extended the filter based active SLAM algorithm to the feature-based deformable environments. The algorithm was designed based on possible assumptions of the feature dynamic model. The designed algorithms were tested and evaluated both in simulation and in the real world in terms of estimation accuracy, coverage, and exploration time.

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http://hdl.handle.net/10453/187505