A GMM based uncertainty model for point clouds registration

Li, Q; Xiong, R; Vidal-Calleja, T

A GMM based uncertainty model for point clouds registration

Li, Q Xiong, R Vidal-Calleja, T

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Publication Type:: Journal Article
Citation:: Robotics and Autonomous Systems, 2017, 91 pp. 349 - 362
Issue Date:: 2017-05-01

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Field	Value	Language
dc.contributor.author	Li, Q	en_US
dc.contributor.author	Xiong, R	en_US
dc.contributor.author	Vidal-Calleja, T https://orcid.org/0000-0002-5763-9644	en_US
dc.date.issued	2017-05-01	en_US
dc.identifier.citation	Robotics and Autonomous Systems, 2017, 91 pp. 349 - 362	en_US
dc.identifier.issn	0921-8890	en_US
dc.identifier.uri	http://hdl.handle.net/10453/78706
dc.description.abstract	© 2016 Elsevier B.V. The existing methods for the registration of point clouds acquired by laser scanners have some limitations. Firstly, as some samples of surface, a point cloud acquired by the laser scanner, which normally works in a spherical fashion, has very limited density when the surface is far away from the laser scanner and the density varies a lot at different ranges. Current registration methods cannot accurately model the surface uncertainty for such kind of point clouds of limited and large varying density. Secondly, when the point cloud is acquired while the platform is simultaneously moving, the estimation error of the platform motion makes the acquired point cloud distorted. To deal with these problems, in this paper, we propose an uncertainty model based on the Gaussian Mixture Model (GMM) to represent the point cloud. Specifically, we construct the GMM piece-wisely on the underlying surface of point cloud, which will accurately model the surface uncertainty. Also a hierarchical structure is employed to increase the robustness of the registration. Furthermore, by assigning each Gaussian component with a pose, a probabilistic graph can be constructed to tackle the problem of registration when the platform is moving while scanning. In this way the distorted point cloud, caused by the estimation error of the platform's motion, can be corrected by performing graph optimization. Simulation and real world experimental results show that our method leads to better convergence than the state-of-the-art methods due to the accurate modeling of the surface uncertainty and the hierarchical structure, and it also enables us to correct the distorted point clouds.	en_US
dc.relation.ispartof	Robotics and Autonomous Systems	en_US
dc.relation.isbasedon	10.1016/j.robot.2016.11.021	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	A GMM based uncertainty model for point clouds registration	en_US
dc.type	Journal Article
utslib.citation.volume	91	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	91	en_US

Abstract:

© 2016 Elsevier B.V. The existing methods for the registration of point clouds acquired by laser scanners have some limitations. Firstly, as some samples of surface, a point cloud acquired by the laser scanner, which normally works in a spherical fashion, has very limited density when the surface is far away from the laser scanner and the density varies a lot at different ranges. Current registration methods cannot accurately model the surface uncertainty for such kind of point clouds of limited and large varying density. Secondly, when the point cloud is acquired while the platform is simultaneously moving, the estimation error of the platform motion makes the acquired point cloud distorted. To deal with these problems, in this paper, we propose an uncertainty model based on the Gaussian Mixture Model (GMM) to represent the point cloud. Specifically, we construct the GMM piece-wisely on the underlying surface of point cloud, which will accurately model the surface uncertainty. Also a hierarchical structure is employed to increase the robustness of the registration. Furthermore, by assigning each Gaussian component with a pose, a probabilistic graph can be constructed to tackle the problem of registration when the platform is moving while scanning. In this way the distorted point cloud, caused by the estimation error of the platform's motion, can be corrected by performing graph optimization. Simulation and real world experimental results show that our method leads to better convergence than the state-of-the-art methods due to the accurate modeling of the surface uncertainty and the hierarchical structure, and it also enables us to correct the distorted point clouds.

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http://hdl.handle.net/10453/78706