Robot localisation in 3D environments using sparse range measurements

Arukgoda, J; Ranasinghe, R; Dissanayake, G

Robot localisation in 3D environments using sparse range measurements

Arukgoda, J Ranasinghe, R

Dissanayake, G

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 2019, 2019-July, pp. 551-558
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Arukgoda, J
dc.contributor.author	Ranasinghe, R https://orcid.org/0000-0002-3785-3723
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680
dc.date	2019-07-08
dc.date.accessioned	2020-06-15T06:50:23Z
dc.date.available	2020-06-15T06:50:23Z
dc.date.issued	2019-07-01
dc.identifier.citation	IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 2019, 2019-July, pp. 551-558
dc.identifier.isbn	9781728124933
dc.identifier.issn	2159-6255
dc.identifier.uri	http://hdl.handle.net/10453/141426
dc.description.abstract	© 2019 IEEE. This paper presents an algorithm for mobile robot localisation given a map of a 3D environment and a sparse set of range-bearing measurements. The environment is represented using a spline approximation of its vector distance function (VDF). For a given location in the environment, VDF encodes the distance to the nearest occupied region along three orthogonal axes. VDF is first obtained from an occupancy voxel map and its three components are then approximated in the least-square sense using a set of three dimensional cubic b-splines, providing a rich and continuous representation of the environment. First and second order derivatives of the VDF are also computed and stored. The difference between an observed range measurement in a given direction and its expected value is formulated as a function of the robot location and the spline coefficients representing the VDF. This leads to a non-linear least-squares optimization problem that can be solved to localise the robot given a set of such measurements. It is demonstrated that a sparse set of range-bearing measurements, an order of magnitude smaller than what is typically available from 3D range sensor is adequate to achieve accurate localisation. The algorithm presented is illustrated using a number of examples including a single point range sensor mounted on a pan-tilt head to localise a robot moving in an indoor environment.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
dc.relation.ispartof	EEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
dc.relation.isbasedon	10.1109/AIM.2019.8868466
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Robot localisation in 3D environments using sparse range measurements
dc.type	Conference Proceeding
utslib.citation.volume	2019-July
utslib.location.activity	Hong Kong
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-15T06:50:09Z
pubs.finish-date	2019-07-12
pubs.publication-status	Published
pubs.start-date	2019-07-08
pubs.volume	2019-July

Abstract:

© 2019 IEEE. This paper presents an algorithm for mobile robot localisation given a map of a 3D environment and a sparse set of range-bearing measurements. The environment is represented using a spline approximation of its vector distance function (VDF). For a given location in the environment, VDF encodes the distance to the nearest occupied region along three orthogonal axes. VDF is first obtained from an occupancy voxel map and its three components are then approximated in the least-square sense using a set of three dimensional cubic b-splines, providing a rich and continuous representation of the environment. First and second order derivatives of the VDF are also computed and stored. The difference between an observed range measurement in a given direction and its expected value is formulated as a function of the robot location and the spline coefficients representing the VDF. This leads to a non-linear least-squares optimization problem that can be solved to localise the robot given a set of such measurements. It is demonstrated that a sparse set of range-bearing measurements, an order of magnitude smaller than what is typically available from 3D range sensor is adequate to achieve accurate localisation. The algorithm presented is illustrated using a number of examples including a single point range sensor mounted on a pan-tilt head to localise a robot moving in an indoor environment.

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http://hdl.handle.net/10453/141426