A hybrid dead reckoning error correction scheme based on extended Kalman filter and map matching for vehicle self-localization. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Yu, Biao; Dong, Lin; Xue, Deyi; Zhu, Hui; Geng, Xinli; Huang, Ruling; Wang, Jie
• Is Part Of: Journal of intelligent transportation systems. Volume 23:Issue 1(2019)
• Page Start: 84
• Page End: 98
• Publication Date: 2019-01-02
• Subjects: Dead reckoning -- extended Kalman filter -- map matching -- self-localization; Intelligent transportation systems -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.312
• DOI: 10.1080/15472450.2018.1527693
• Languages: English
• ISSNs: 1547-2450

Abstract

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.