InforLorV4, Hal, Corpus, bibRecord, 002B89

Intelligent geo-localisation in urban areas using global positioning systems, 3dimensional geographic information systems, and vision

Identifieur interne : 002B89 ( Hal/Corpus ); précédent : 002B88; suivant : 002B90

Intelligent geo-localisation in urban areas using global positioning systems, 3dimensional geographic information systems, and vision

Auteurs : Cindy Cappelle ; Maan El Badaoui El Najjar ; Denis Pomorski ; François Charpillet

Source :

Journal of Intelligent Transportation Systems: Technology, Planning, and Operations [ 1547-2450 ] ; 2010.

RBID : Hal:inria-00441814

Abstract

This article tackles the problem of a vehicle's geolocalization in urban areas. For this purpose, Global Positioning System (GPS) receiver is the main sensor. However, the use of GPS alone is not sufficient in many urban environments. GPS has to be helped with dead-reckoned sensors, map data, and cameras. A novel observation of the absolute pose of the vehicle is proposed to back up GPS and the drift of dead-reckoned sensors. This approach uses a new source of information that is a geographical 3-dimensional (3D) model of the environment in which the vehicle navigates. This virtual 3D city model is managed in real time by a 3D geographical information system (3D GIS). This pose's observation is constructed by matching the virtual image provided by the 3D GIS and the real image acquired by an onboard camera. An extended Kalman filter combines the sensors measurements to produce an estimation of the vehicle's pose. Experimental results using data from an odometer, a gyroscope, a GPS receiver, a camera, and an accurate geographical 3D model of the environment illustrate the developed approach.

Url:

https://hal.inria.fr/inria-00441814

DOI: 10.1080/15472450903385999

Links to Exploration step

Hal:inria-00441814

Le document en format XML

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<front><div type="abstract" xml:lang="en">This article tackles the problem of a vehicle's geolocalization in urban areas. For this purpose, Global Positioning System (GPS) receiver is the main sensor. However, the use of GPS alone is not sufficient in many urban environments. GPS has to be helped with dead-reckoned sensors, map data, and cameras. A novel observation of the absolute pose of the vehicle is proposed to back up GPS and the drift of dead-reckoned sensors. This approach uses a new source of information that is a geographical 3-dimensional (3D) model of the environment in which the vehicle navigates. This virtual 3D city model is managed in real time by a 3D geographical information system (3D GIS). This pose's observation is constructed by matching the virtual image provided by the 3D GIS and the real image acquired by an onboard camera. An extended Kalman filter combines the sensors measurements to produce an estimation of the vehicle's pose. Experimental results using data from an odometer, a gyroscope, a GPS receiver, a camera, and an accurate geographical 3D model of the environment illustrate the developed approach.</div>
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<hal api="V3"><titleStmt><title xml:lang="en">Intelligent geo-localisation in urban areas using global positioning systems, 3dimensional geographic information systems, and vision</title>
<author role="aut"><persName><forename type="first">Cindy</forename>
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<textClass><classCode scheme="halDomain" n="info.info-rb">Computer Science [cs]/Robotics [cs.RO]</classCode>
<classCode scheme="halTypology" n="ART">Journal articles</classCode>
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<abstract xml:lang="en">This article tackles the problem of a vehicle's geolocalization in urban areas. For this purpose, Global Positioning System (GPS) receiver is the main sensor. However, the use of GPS alone is not sufficient in many urban environments. GPS has to be helped with dead-reckoned sensors, map data, and cameras. A novel observation of the absolute pose of the vehicle is proposed to back up GPS and the drift of dead-reckoned sensors. This approach uses a new source of information that is a geographical 3-dimensional (3D) model of the environment in which the vehicle navigates. This virtual 3D city model is managed in real time by a 3D geographical information system (3D GIS). This pose's observation is constructed by matching the virtual image provided by the 3D GIS and the real image acquired by an onboard camera. An extended Kalman filter combines the sensors measurements to produce an estimation of the vehicle's pose. Experimental results using data from an odometer, a gyroscope, a GPS receiver, a camera, and an accurate geographical 3D model of the environment illustrate the developed approach.</abstract>
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Intelligent geo-localisation in urban areas using global positioning systems, 3dimensional geographic information systems, and vision

Intelligent geo-localisation in urban areas using global positioning systems, 3dimensional geographic information systems, and vision

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