Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A hybrid dead reckoning error correction scheme based on extended Kalman filter and map matching for vehicle self-localization

A hybrid dead reckoning error correction scheme based on extended Kalman filter and map matching... AbstractIn 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent Transportation Systems Taylor & Francis

A hybrid dead reckoning error correction scheme based on extended Kalman filter and map matching for vehicle self-localization

A hybrid dead reckoning error correction scheme based on extended Kalman filter and map matching for vehicle self-localization

Journal of Intelligent Transportation Systems , Volume 23 (1): 15 – Jan 2, 2019

Abstract

AbstractIn 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.

Loading next page...
 
/lp/taylor-francis/a-hybrid-dead-reckoning-error-correction-scheme-based-on-extended-j3Z70PargD

References (28)

Publisher
Taylor & Francis
Copyright
© 2018 Taylor & Francis Group, LLC
ISSN
1547-2442
eISSN
1547-2450
DOI
10.1080/15472450.2018.1527693
Publisher site
See Article on Publisher Site

Abstract

AbstractIn 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.

Journal

Journal of Intelligent Transportation SystemsTaylor & Francis

Published: Jan 2, 2019

Keywords: Dead reckoning; extended Kalman filter; map matching; self-localization

There are no references for this article.