A Road-Matching Method for Precise Vehicle ...
Document type :
Article dans une revue scientifique
Title :
A Road-Matching Method for Precise Vehicle Localization using Belief Theory and Kalman Filtering
Author(s) :
El Badaoui El Najjar, Maan [Auteur]
Systèmes Tolérants aux Fautes [STF]
Bonnifait, Philippe [Auteur]

Systèmes Tolérants aux Fautes [STF]
Bonnifait, Philippe [Auteur]
Journal title :
Autonomous Robots
Publisher :
Springer Verlag
Publication date :
2005
ISSN :
0929-5593
HAL domain(s) :
Informatique [cs]/Automatique
Informatique [cs]/Robotique [cs.RO]
Informatique [cs]/Robotique [cs.RO]
English abstract : [en]
This paper describes a novel road-matching method designed to support the real-time navigational function of cars for advanced systems applications in the area of driving assistance. This method provides an accurate ...
Show more >This paper describes a novel road-matching method designed to support the real-time navigational function of cars for advanced systems applications in the area of driving assistance. This method provides an accurate estimation of position for a vehicle relative to a digital road map using Belief Theory and Kalman filtering. Firstly, an Extended Kalman Filter combines the DGPS and ABS sensor measurements to produce an approximation of the vehicle's pose, which is then used to select the most likely segment from the database. The selection strategy merges several criteria based on distance, direction and velocity measurements using Belief Theory. A new observation is then built using the selected segment, and the approximate pose adjusted in a second Kalman filter estimation stage. The particular attention given to the modeling of the system showed that incrementing the state by the bias (also called absolute error) of the map significantly increases the performance of the method. Real experimental results show that this approach, if correctly initialized, is able to work over a substantial period without GPS.Show less >
Show more >This paper describes a novel road-matching method designed to support the real-time navigational function of cars for advanced systems applications in the area of driving assistance. This method provides an accurate estimation of position for a vehicle relative to a digital road map using Belief Theory and Kalman filtering. Firstly, an Extended Kalman Filter combines the DGPS and ABS sensor measurements to produce an approximation of the vehicle's pose, which is then used to select the most likely segment from the database. The selection strategy merges several criteria based on distance, direction and velocity measurements using Belief Theory. A new observation is then built using the selected segment, and the approximate pose adjusted in a second Kalman filter estimation stage. The particular attention given to the modeling of the system showed that incrementing the state by the bias (also called absolute error) of the map significantly increases the performance of the method. Real experimental results show that this approach, if correctly initialized, is able to work over a substantial period without GPS.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :