@inproceedings{c1edad09e7b84f21876997c2e3fc2e10,
title = "An improved binocular visual odometry for high-speed automotive applications",
abstract = "In this paper, we present an improved motion estimation method by adding extra information for binocular visual odometry (VO) which is especially suited for improving high-speed pose change estimation. The extra information is obtained by structured object detecting, taking lane line detection as an example. We can get an accurate position information by calculating the interval of each dotted lane line and counting the number of the dotted line which can be fused with the pose information obtained from visual odometry. The outlier rejection of the VO is also improved, making it adapt to highway situation. In the fusion process, a Kalman filter is adopted to estimate the motion and location information for a high speed vehicle. The experimental results show that the approach proposed is valid and can increase the positioning accuracy significantly compared with ordinary visual odometry.",
keywords = "Kalman Filter, Lane Line Detection, Visual Odometry",
author = "Yu Huan and Chen Jiabin and Wang Liujun and Xie Ling and Song Chunlei and Wu Qinghe",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 29th Chinese Control and Decision Conference, CCDC 2017 ; Conference date: 28-05-2017 Through 30-05-2017",
year = "2017",
month = jul,
day = "12",
doi = "10.1109/CCDC.2017.7978089",
language = "English",
series = "Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "185--190",
booktitle = "Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017",
address = "United States",
}