A novel road-matching method was 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 (Differential Global Positioning System) and ABS (Anti-Block System) 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.
A road-matching method for precise vehicle localization using Belief Theory and Kalman filtering
Eine Straßenauswahlmethode für die exakte Fahrzeuglokalisation mittels Wahrscheinlichkeitstheorie und Kalman-Filter
Autonomous Robots ; 19 , 2 ; 173-191
2005
19 Seiten, 26 Quellen
Article (Journal)
English
A Road-Matching Method for Precise Vehicle Localization Using Belief Theory and Kalman Filtering
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