A framework for global vehicle localization using stereo images and satellite and road maps

Turgay Senlet; Ahmed Elgammal

doi:https://doi.org/10.1109/ICCVW.2011.6130498

A framework for global vehicle localization using stereo images and satellite and road maps

Turgay Senlet, Ahmed Elgammal

SAS - Computer Science

Rutgers, The State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

32 Scopus citations

Abstract

We present a framework for global vehicle localization and 3D point cloud reconstruction that combines stereo visual odometry, satellite images, and road maps under a particle-filtering architecture. The framework focuses on the general vehicle localization scenario without the use of global positioning system for urban and rural environments and with the presence of moving objects. The main novelties of our approach are using road maps and rendering accurate top views using stereo reconstruction, and match these views with the satellite images in order to eliminate drifts and obtain accurate global localization. We show that our method is practicable by presenting experimental results on a 2 km road where mostly specific road features do not exist.

Original language	English (US)
Title of host publication	2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
Pages	2034-2041
Number of pages	8
DOIs	https://doi.org/10.1109/ICCVW.2011.6130498
State	Published - 2011
Event	2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 - Barcelona, Spain Duration: Nov 6 2011 → Nov 13 2011

Publication series

Name	Proceedings of the IEEE International Conference on Computer Vision

Other

Other	2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
Country/Territory	Spain
City	Barcelona
Period	11/6/11 → 11/13/11

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

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https://doi.org/10.1109/ICCVW.2011.6130498

Cite this

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title = "A framework for global vehicle localization using stereo images and satellite and road maps",

abstract = "We present a framework for global vehicle localization and 3D point cloud reconstruction that combines stereo visual odometry, satellite images, and road maps under a particle-filtering architecture. The framework focuses on the general vehicle localization scenario without the use of global positioning system for urban and rural environments and with the presence of moving objects. The main novelties of our approach are using road maps and rendering accurate top views using stereo reconstruction, and match these views with the satellite images in order to eliminate drifts and obtain accurate global localization. We show that our method is practicable by presenting experimental results on a 2 km road where mostly specific road features do not exist.",

author = "Turgay Senlet and Ahmed Elgammal",

year = "2011",

doi = "https://doi.org/10.1109/ICCVW.2011.6130498",

language = "English (US)",

isbn = "9781467300629",

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pages = "2034--2041",

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note = "2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 ; Conference date: 06-11-2011 Through 13-11-2011",

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Senlet, T & Elgammal, A 2011, A framework for global vehicle localization using stereo images and satellite and road maps. in 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011., 6130498, Proceedings of the IEEE International Conference on Computer Vision, pp. 2034-2041, 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011, Barcelona, Spain, 11/6/11. https://doi.org/10.1109/ICCVW.2011.6130498

A framework for global vehicle localization using stereo images and satellite and road maps. / Senlet, Turgay; Elgammal, Ahmed.

2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011. 2011. p. 2034-2041 6130498 (Proceedings of the IEEE International Conference on Computer Vision).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - We present a framework for global vehicle localization and 3D point cloud reconstruction that combines stereo visual odometry, satellite images, and road maps under a particle-filtering architecture. The framework focuses on the general vehicle localization scenario without the use of global positioning system for urban and rural environments and with the presence of moving objects. The main novelties of our approach are using road maps and rendering accurate top views using stereo reconstruction, and match these views with the satellite images in order to eliminate drifts and obtain accurate global localization. We show that our method is practicable by presenting experimental results on a 2 km road where mostly specific road features do not exist.

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A framework for global vehicle localization using stereo images and satellite and road maps

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