Localising PMDs through CNN Based Perception of Urban Streets

Jayasuriya, M; Arukgoda, J; Ranasinghe, R; Dissanayake, G

Localising PMDs through CNN Based Perception of Urban Streets

Jayasuriya, M Arukgoda, J

Ranasinghe, R

Dissanayake, G

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, 00, pp. 6454-6460
Issue Date:: 2020-09-15

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Field	Value	Language
dc.contributor.author	Jayasuriya, M
dc.contributor.author	Arukgoda, J https://orcid.org/0000-0003-3521-8133
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	2020-05-31
dc.date.accessioned	2021-04-17T20:12:51Z
dc.date.available	2021-04-17T20:12:51Z
dc.date.issued	2020-09-15
dc.identifier.citation	2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, 00, pp. 6454-6460
dc.identifier.isbn	978-1-7281-7396-2
dc.identifier.issn	1050-4729
dc.identifier.uri	http://hdl.handle.net/10453/148183
dc.description.abstract	The main contribution of this paper is a novel Extended Kalman Filter (EKF) based localisation scheme that fuses two complementary approaches to outdoor vision based localisation. This EKF is aided by a front end consisting of two Convolutional Neural Networks (CNNs) that provide the necessary perceptual information from camera images. The first approach involves a CNN based extraction of information corresponding to artefacts such as curbs, lane markings, and manhole covers to localise on a vector distance transform representation of a binary image of these ground surface boundaries. The second approach involves a CNN based detection of common environmental landmarks such as tree trunks and light poles, which are represented as point features on a sparse map. Utilising CNNs to obtain higher level information about the environment enables this framework to avoid the typical pitfalls of common vision based approaches that use low level hand crafted features for localisation. The EKF framework makes it possible to deal with false positives and missed detections that are inevitable in a practical CNN, to produce a location estimate together with its associated uncertainty. Experiments using a Personal Mobility Device (PMD) driven in typical suburban streets are presented to demonstrate the effectiveness of the proposed localiser.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE International Conference on Robotics and Automation (ICRA)
dc.relation.ispartof	IEEE International Conference on Robotics and Automation
dc.relation.isbasedon	10.1109/icra40945.2020.9196639
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Localising PMDs through CNN Based Perception of Urban Streets
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Paris, France
utslib.for	080101 Adaptive Agents and Intelligent Robotics
utslib.for	091302 Automation and Control Engineering
pubs.organisational-group	/University of Technology Sydney
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
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-17T20:12:48Z
pubs.finish-date	2020-08-31
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2020-05-31
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

The main contribution of this paper is a novel Extended Kalman Filter (EKF) based localisation scheme that fuses two complementary approaches to outdoor vision based localisation. This EKF is aided by a front end consisting of two Convolutional Neural Networks (CNNs) that provide the necessary perceptual information from camera images. The first approach involves a CNN based extraction of information corresponding to artefacts such as curbs, lane markings, and manhole covers to localise on a vector distance transform representation of a binary image of these ground surface boundaries. The second approach involves a CNN based detection of common environmental landmarks such as tree trunks and light poles, which are represented as point features on a sparse map. Utilising CNNs to obtain higher level information about the environment enables this framework to avoid the typical pitfalls of common vision based approaches that use low level hand crafted features for localisation. The EKF framework makes it possible to deal with false positives and missed detections that are inevitable in a practical CNN, to produce a location estimate together with its associated uncertainty. Experiments using a Personal Mobility Device (PMD) driven in typical suburban streets are presented to demonstrate the effectiveness of the proposed localiser.

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http://hdl.handle.net/10453/148183