Triplet-Graph: Global Metric Localization Based on Semantic Triplet Graph for Autonomous Vehicles

Ma, W; Huang, S; Sun, Y

Triplet-Graph: Global Metric Localization Based on Semantic Triplet Graph for Autonomous Vehicles

Ma, W Huang, S

Sun, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2024, 9, (4), pp. 3155-3162
Issue Date:: 2024-04-01

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Field	Value	Language
dc.contributor.author	Ma, W
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.contributor.author	Sun, Y
dc.date.accessioned	2024-08-07T04:46:26Z
dc.date.available	2024-08-07T04:46:26Z
dc.date.issued	2024-04-01
dc.identifier.citation	IEEE Robotics and Automation Letters, 2024, 9, (4), pp. 3155-3162
dc.identifier.issn	2377-3766
dc.identifier.issn	2377-3766
dc.identifier.uri	http://hdl.handle.net/10453/180230
dc.description.abstract	Global metric localization is one of the fundamental capabilities for autonomous vehicles. Most existing methods rely on global navigation satellite systems (GNSS). Some methods relieve the need of GNSS by using 3-D LiDARs. They first achieve place recognition with a pre-built geo-referenced point-cloud database for coarse global localization, and then achieve 3-DoF/6-DoF pose estimation for fine-grained metric localization. However, these methods require accessing point-cloud features and raw point clouds, making them inefficient and hard to be deployed in large-scale environments. To provide a solution to this issue, we propose a global metric localization method with triplet-based histogram descriptors. Specifically, we first convert the input LiDAR point clouds into a semantic graph and describe the vertices in the graph with the proposed descriptor for vertex matching and pose estimation. These vertex descriptors are then selected and aggregated into a global descriptor to decide whether two places correspond to the same place according to a similarity score. Experimental results on the KITTI dataset demonstrate that our method generally outperforms the sate-of-the-art methods.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Robotics and Automation Letters
dc.relation.isbasedon	10.1109/LRA.2024.3358752
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0913 Mechanical Engineering
dc.subject.classification	4007 Control engineering, mechatronics and robotics
dc.subject.classification	4602 Artificial intelligence
dc.title	Triplet-Graph: Global Metric Localization Based on Semantic Triplet Graph for Autonomous Vehicles
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0913 Mechanical 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 - RI - Robotics Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-07T04:46:19Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	4

Abstract:

Global metric localization is one of the fundamental capabilities for autonomous vehicles. Most existing methods rely on global navigation satellite systems (GNSS). Some methods relieve the need of GNSS by using 3-D LiDARs. They first achieve place recognition with a pre-built geo-referenced point-cloud database for coarse global localization, and then achieve 3-DoF/6-DoF pose estimation for fine-grained metric localization. However, these methods require accessing point-cloud features and raw point clouds, making them inefficient and hard to be deployed in large-scale environments. To provide a solution to this issue, we propose a global metric localization method with triplet-based histogram descriptors. Specifically, we first convert the input LiDAR point clouds into a semantic graph and describe the vertices in the graph with the proposed descriptor for vertex matching and pose estimation. These vertex descriptors are then selected and aggregated into a global descriptor to decide whether two places correspond to the same place according to a similarity score. Experimental results on the KITTI dataset demonstrate that our method generally outperforms the sate-of-the-art methods.

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http://hdl.handle.net/10453/180230