On the use of 2D navigable maps for enhancing ground vehicle localization

For ground vehicle localization, hybrid-GNSS localizers now use commonly dead-reckoning sensors, like odometers or inertial units. They are designed to increase the accuracy, the integrity and the availability of the localization information, particularly in areas where the satellite signals are sub...

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Published in2009 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 1885 - 1890
Main Authors Fouque, C., Bonnifait, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2009
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Abstract For ground vehicle localization, hybrid-GNSS localizers now use commonly dead-reckoning sensors, like odometers or inertial units. They are designed to increase the accuracy, the integrity and the availability of the localization information, particularly in areas where the satellite signals are subject to outages and multipaths. In this paper, a data-fusion method is proposed to take benefits of 2D navigable road-maps in a tightly-coupled approach. In such a problem, 3D modeling is mandatory to process the pseudo-range information of the satellites. Our proposal is to use a 2D map as a heading measure in a Earth tangential frame. This is called ¿map-aided odometry¿. A Kalman filter, gating the normalized innovation signals, is applied to merge the redundant exteroceptive information in a cautious way. Experimental results are reported to quantify the performance gain of the proposed approach relying on the map-aided technique. We show that this fusion scheme increases the accuracy and the availability of the provided localization specially in GNSS-challenging environments.
AbstractList For ground vehicle localization, hybrid-GNSS localizers now use commonly dead-reckoning sensors, like odometers or inertial units. They are designed to increase the accuracy, the integrity and the availability of the localization information, particularly in areas where the satellite signals are subject to outages and multipaths. In this paper, a data-fusion method is proposed to take benefits of 2D navigable road-maps in a tightly-coupled approach. In such a problem, 3D modeling is mandatory to process the pseudo-range information of the satellites. Our proposal is to use a 2D map as a heading measure in a Earth tangential frame. This is called ¿map-aided odometry¿. A Kalman filter, gating the normalized innovation signals, is applied to merge the redundant exteroceptive information in a cautious way. Experimental results are reported to quantify the performance gain of the proposed approach relying on the map-aided technique. We show that this fusion scheme increases the accuracy and the availability of the provided localization specially in GNSS-challenging environments.
Author Fouque, C.
Bonnifait, P.
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Snippet For ground vehicle localization, hybrid-GNSS localizers now use commonly dead-reckoning sensors, like odometers or inertial units. They are designed to...
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StartPage 1885
SubjectTerms Earth
Global Positioning System
Intelligent robots
Intelligent sensors
Land vehicles
Proposals
Road vehicles
Satellite navigation systems
Technological innovation
USA Councils
Title On the use of 2D navigable maps for enhancing ground vehicle localization
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