SkyLoc: Cross-Modal Global Localization With a Sky-Looking Fish-Eye Camera and OpenStreetMap

Ma, W; Huang, S; Sun, Y

SkyLoc: Cross-Modal Global Localization With a Sky-Looking Fish-Eye Camera and OpenStreetMap

Ma, W Huang, S

Sun, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Intelligent Transportation Systems, 2025, 26, (5), pp. 5832-5842
Issue Date:: 2025-01-01

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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	2025-07-31T05:13:10Z
dc.date.available	2025-07-31T05:13:10Z
dc.date.issued	2025-01-01
dc.identifier.citation	IEEE Transactions on Intelligent Transportation Systems, 2025, 26, (5), pp. 5832-5842
dc.identifier.issn	1524-9050
dc.identifier.issn	1558-0016
dc.identifier.uri	http://hdl.handle.net/10453/188903
dc.description.abstract	Global localization can estimate geo-referenced locations (e.g., longitude and latitude), which is a fundamental capability for autonomous vehicles. Most existing solutions rely on the Global Navigation Satellite Systems (GNSS). Their accuracy could be degraded by the multi-path effects or occlusions of GNSS signals in urban environments. Some GNSS-free methods could achieve global localization by comparing the current on-line sensory data with pre-built databases/maps. However, they require tedious human efforts to drive a vehicle to collect and maintain the databases/maps. Moreover, most of these methods use front-looking cameras or LiDARs, so the captured data could be easily contaminated by dynamic objects (e.g., moving vehicles and pedestrians). To provide a solution to these problems, this paper proposes a novel global localization method by comparing an image from a sky-looking fish-eye camera with the publicly available OpenStreetMap (OSM), and using particle filter to achieve real-time metric localization in dynamic traffic environments. To evaluate our method, we extend a public dataset with OSM data, which are retrieved through the given geo-referenced location information. Experimental results demonstrate the effectiveness and efficiency of our method.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Intelligent Transportation Systems
dc.relation.isbasedon	10.1109/TITS.2025.3550941
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0905 Civil Engineering, 1507 Transportation and Freight Services
dc.subject.classification	Logistics & Transportation
dc.subject.classification	3509 Transportation, logistics and supply chains
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4603 Computer vision and multimedia computation
dc.title	SkyLoc: Cross-Modal Global Localization With a Sky-Looking Fish-Eye Camera and OpenStreetMap
dc.type	Journal Article
utslib.citation.volume	26
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0905 Civil Engineering
utslib.for	1507 Transportation and Freight Services
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/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Robotics Institute (RI)
utslib.copyright.status	open_access	*
dc.date.updated	2025-07-31T05:13:07Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	26
utslib.citation.issue	5

Abstract:

Global localization can estimate geo-referenced locations (e.g., longitude and latitude), which is a fundamental capability for autonomous vehicles. Most existing solutions rely on the Global Navigation Satellite Systems (GNSS). Their accuracy could be degraded by the multi-path effects or occlusions of GNSS signals in urban environments. Some GNSS-free methods could achieve global localization by comparing the current on-line sensory data with pre-built databases/maps. However, they require tedious human efforts to drive a vehicle to collect and maintain the databases/maps. Moreover, most of these methods use front-looking cameras or LiDARs, so the captured data could be easily contaminated by dynamic objects (e.g., moving vehicles and pedestrians). To provide a solution to these problems, this paper proposes a novel global localization method by comparing an image from a sky-looking fish-eye camera with the publicly available OpenStreetMap (OSM), and using particle filter to achieve real-time metric localization in dynamic traffic environments. To evaluate our method, we extend a public dataset with OSM data, which are retrieved through the given geo-referenced location information. Experimental results demonstrate the effectiveness and efficiency of our method.

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http://hdl.handle.net/10453/188903