Title
A hybrid dead reckoning error correction scheme based on extended Kalman filter and map matching for vehicle self-localization
Abstract
In this research, a hybrid dead reckoning error correction scheme is developed based on extended Kalman filter (EKF) and map matching (MM) to improve the positioning accuracy for vehicle self-localization. The developed method aims at obtaining accurate positions when the GPS signals are occasionally unavailable or weakened. First, the heading data collected from an odometer and an optical fiber gyroscope are integrated by an EKF to reduce the random errors in dead reckoning. Then a modified topological MM algorithm is developed to reduce the systematic errors in dead reckoning. In this work, both cross-track errors and along-track errors are considered to improve positioning accuracy of MM. The errors are finally corrected using the results achieved from both the dead reckoning and the MM when the driving distance of a vehicle exceeds a predefined length or the vehicle turns in an intersection. Experiments have been conducted to evaluate the developed method and the results show that the maximum error and average error of dead reckoning can be respectively reduced to 15.4 m and 5.2 m during the experiment with total distance of 43 km. This positioning accuracy is even better than the accuracy of the low-cost GPSs which are usually at the order of 15–20 m (95%). The developed method is effective to achieve the positions of the vehicle when the GPS signals are occasionally unavailable or weakened.
Year
DOI
Venue
2019
10.1080/15472450.2018.1527693
Journal of Intelligent Transportation Systems
Keywords
Field
DocType
Dead reckoning,extended Kalman filter,map matching,self-localization
Computer vision,Gyroscope,Extended Kalman filter,Simulation,Error detection and correction,Dead reckoning,MM algorithm,Artificial intelligence,Engineering,GPS signals,Map matching,Odometer
Journal
Volume
Issue
ISSN
23
1
1547-2450
Citations 
PageRank 
References 
0
0.34
12
Authors
7
Name
Order
Citations
PageRank
Biao Yu111.71
Lin Dong200.34
Deyi Xue315019.11
Hui Zhu400.34
Xinli Geng500.34
Ruling Huang600.34
Jie Wang7213.04