As one of the key requirements in the intelligent vehicle, accurate and precise localisation is essential to ensure swift route planning during the drive. In this study, the authors would like to reduce the longitudinal positioning error that remains as a challenge in accurate localisation. To solve this, they propose a data fusion method by integrating information from visual odometry (VO), noisy GPS, and road information obtained from the publicly available digital map with particle filter. The curve of the VO trajectory trail is compared with road segments curve to increase longitudinal accuracy. This method is validated by KITTI dataset, tested with different GPS noise conditions, and the results show improved localisation for both lateral and longitudinal positioning errors.
Longitudinal error improvement by visual odometry trajectory trail and road segment matching
IET Intelligent Transport Systems ; 13 , 2 ; 313-322
2018-10-29
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
road information , road segment matching , longitudinal positioning errors , road vehicles , Global Positioning System , sensor fusion , GPS noise conditions , particle filtering (numerical methods) , distance measurement , digital map , data fusion method , road segments curve , intelligent vehicle , noisy GPS , visual odometry trajectory trail , lateral positioning errors , longitudinal error improvement , VO trajectory trail , swift route planning
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Longitudinal error improvement by visual odometry trajectory trail and road segment matching
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