Longitudinal error improvement by visual odometry trajectory trail and road segment matching

Dayang Nur Salmi Dharmiza Awang Salleh (1) , Emmanuel Seignez (1)

1 SATIE - Systèmes et Applications des Technologies de l'Information et de l'Energie

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.

Keywords: distance measurement; particle filtering (numerical methods); Global Positioning System; road vehicles; sensor fusion; swift route planning; data fusion method; lateral positioning errors; road segment matching; GPS noise conditions; digital map; visual odometry trajectory trail; longitudinal error improvement; longitudinal positioning errors; VO trajectory trail; road information; noisy GPS

Domains: Engineering Sciences [physics]; Engineering Sciences [physics]; Electric power; Engineering Sciences [physics]; Signal and Image processing; Engineering Sciences [physics]; Electronics

Dayang Nur Salmi Dharmiza Awang Salleh, Emmanuel Seignez. Longitudinal error improvement by visual odometry trajectory trail and road segment matching. IET Intelligent Transport Systems, 2019, 13 (2), pp.313-322. ⟨10.1049/iet-its.2018.5272⟩. ⟨hal-04026569⟩