Uncertainty Estimation of Dense Optical Flow for Robust Visual Navigation.

Ng, Y; Li, H; Kim, J

Uncertainty Estimation of Dense Optical Flow for Robust Visual Navigation.

Ng, Y Li, H Kim, J

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors, 2021, 21, (22), pp. 1-16
Issue Date:: 2021-11-16

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Field	Value	Language
dc.contributor.author	Ng, Y
dc.contributor.author	Li, H
dc.contributor.author	Kim, J https://orcid.org/0000-0003-4505-1103
dc.date.accessioned	2022-05-27T04:12:55Z
dc.date.available	2021-11-09
dc.date.available	2022-05-27T04:12:55Z
dc.date.issued	2021-11-16
dc.identifier.citation	Sensors, 2021, 21, (22), pp. 1-16
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/157749
dc.description.abstract	This paper presents a novel dense optical-flow algorithm to solve the monocular simultaneous localisation and mapping (SLAM) problem for ground or aerial robots. Dense optical flow can effectively provide the ego-motion of the vehicle while enabling collision avoidance with the potential obstacles. Existing research has not fully utilised the uncertainty of the optical flow-at most, an isotropic Gaussian density model has been used. We estimate the full uncertainty of the optical flow and propose a new eight-point algorithm based on the statistical Mahalanobis distance. Combined with the pose-graph optimisation, the proposed method demonstrates enhanced robustness and accuracy for the public autonomous car dataset (KITTI) and aerial monocular dataset.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors
dc.relation.isbasedon	10.3390/s21227603
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.mesh	Algorithms
dc.subject.mesh	Motion
dc.subject.mesh	Optic Flow
dc.subject.mesh	Uncertainty
dc.subject.mesh	Algorithms
dc.subject.mesh	Motion
dc.subject.mesh	Optic Flow
dc.subject.mesh	Uncertainty
dc.subject.mesh	Uncertainty
dc.subject.mesh	Algorithms
dc.subject.mesh	Motion
dc.subject.mesh	Optic Flow
dc.title	Uncertainty Estimation of Dense Optical Flow for Robust Visual Navigation.
dc.type	Journal Article
utslib.citation.volume	21
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic 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
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-05-27T04:12:47Z
pubs.issue	22
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	22

Abstract:

This paper presents a novel dense optical-flow algorithm to solve the monocular simultaneous localisation and mapping (SLAM) problem for ground or aerial robots. Dense optical flow can effectively provide the ego-motion of the vehicle while enabling collision avoidance with the potential obstacles. Existing research has not fully utilised the uncertainty of the optical flow-at most, an isotropic Gaussian density model has been used. We estimate the full uncertainty of the optical flow and propose a new eight-point algorithm based on the statistical Mahalanobis distance. Combined with the pose-graph optimisation, the proposed method demonstrates enhanced robustness and accuracy for the public autonomous car dataset (KITTI) and aerial monocular dataset.

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http://hdl.handle.net/10453/157749