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Cited by 85Robotics and Sensor-Based LocalizationAutonomous Vehicle Technology and Safety3D Surveying and Cultural Heritage

Vehicle localization using mono-camera and geo-referenced traffic signs

Qu, Xiaozhi, Soheilian, Bahman, Paparoditis, Nicolas·2015·2015 IEEE Intelligent Vehicles Symposium (IV)
DOI 10.1109/ivs.2015.7225751
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Abstract

Vision based localization is a cost effective method for indoor and outdoor application. However, it has drift problem if none global optimization is used. We proposed a geo-referenced traffic sign based localization method, which integrated the constraints of 3D traffic signs with local bundle adjustment to reduce the drift. Comparing to global bundle adjustment, Local Bundle Adjustment(LBA) has low computational cost but suffers the drift problem for large scale localization because of the random error accumulation. We reduced the drift by means of the constraints from geo-referenced traffic signs for bundle adjustment process. The original LBA model was extended for the constraints and the traffic signs were detected in images and matched with 3D landmark database automatically. From the experiments of simulated and real images, our approach can reduce the drift and have better locating results than none-constraint LBA based localization method.

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Authors
Qu, X., Soheilian, B., & Paparoditis, N.
Affiliations
Institut national de l’information géographique et forestière (FR)
ORCID
Soheilian, Bahman
Venue
2015 IEEE Intelligent Vehicles Symposium (IV)
Publisher
IEEE
Published
2015-06
Pages
605-610
ISSN
1931-0587 (ISSN-L)
DOI
10.1109/ivs.2015.7225751
OpenAlex
W1556546299
Cited by
85
References
2
Citations by year
2025: 4 · 2024: 6 · 2023: 8 · 2022: 1 · 2021: 10 · 2020: 14 · 2019: 11 · 2018: 8 · 2017: 12 · 2016: 9 · 2015: 2
FWCI
17.91
Type
conference paper
Language
en
Open access
closed
Locations
IEEE Intelligent Vehicles Symposium (publishedVersion)
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
Keywords
vehicle localization, visual localization, drift reduction, traffic sign detection

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