Winkvist, Stefan (2013) Low computational SLAM for an autonomous indoor aerial inspection vehicle. PhD thesis, University of Warwick.

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Abstract

The past decade has seen an increase in the capability of small scale Unmanned
Aerial Vehicle (UAV) systems, made possible through technological advancements
in battery, computing and sensor miniaturisation technology. This has opened a new
and rapidly growing branch of robotic research and has sparked the imagination of
industry leading to new UAV based services, from the inspection of power-lines to
remote police surveillance.
Miniaturisation of UAVs have also made them small enough to be practically flown
indoors. For example, the inspection of elevated areas in hazardous or damaged
structures where the use of conventional ground-based robots are unsuitable. Sellafield
Ltd, a nuclear reprocessing facility in the U.K. has many buildings that require
frequent safety inspections. UAV inspections eliminate the current risk to personnel
of radiation exposure and other hazards in tall structures where scaffolding or hoists
are required.
This project focused on the development of a UAV for the novel application of
semi-autonomously navigating and inspecting these structures without the need for
personnel to enter the building. Development exposed a significant gap in knowledge
concerning indoor localisation, specifically Simultaneous Localisation and Mapping
(SLAM) for use on-board UAVs. To lower the on-board processing requirements
of SLAM, other UAV research groups have employed techniques such as off-board
processing, reduced dimensionality or prior knowledge of the structure, techniques
not suitable to this application given the unknown nature of the structures and the
risk of radio-shadows.
In this thesis a novel localisation algorithm, which enables real-time and threedimensional
SLAM running solely on-board a computationally constrained UAV in
heavily cluttered and unknown environments is proposed. The algorithm, based
on the Iterative Closest Point (ICP) method utilising approximate nearest neighbour
searches and point-cloud decimation to reduce the processing requirements has
successfully been tested in environments similar to that specified by Sellafield Ltd.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QA Mathematics T Technology > TJ Mechanical engineering and machinery
Library of Congress Subject Headings (LCSH):	Mobile robots -- Automatic control, Robots -- Control systems, Autonomous robots, Mappings (Mathematics), Iterative methods (Mathematics), Algorithms
Official Date:	November 2013
Institution:	University of Warwick
Theses Department:	Warwick Manufacturing Group
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Rushforth, Emma J.; Young, K. (Ken)
Sponsors:	Sellafield Ltd.; Engineering and Physical Sciences Research Council (EPSRC); Warwick Innovative Manufacturing Research Centre (WIMRC)
Extent:	xxiii, 229 leaves : illustrations.
Language:	eng

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