TY - GEN
T1 - Online high-accurate calibration of RGB+3D-LiDAR for autonomous driving
AU - Li, Tao
AU - Fang, Jianwu
AU - Zhong, Yang
AU - Wang, Di
AU - Xue, Jianru
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Vision+X has become the promising tendency for scene understanding in autonomous driving, where X may be the other non-vision sensors. However, it is difficult to utilize all the superiority of different sensors, mainly because of the heterogenous, asynchronous properties. To this end, this paper calibrates the commonly used RGB+3D-LiDAR data by synchronization and an online spatial structure alignment, and obtains a high-accurate calibration performance. The main highlights are that (1) we rectify the 3D points with the aid of differential inertial measurement unit (IMU), and increase the frequency of 3D laser data as the same as the ones of RGB data, and (2) this work can online high-accurately updates the external parameters of calibration by a more reliable spatial-structure matching of RGB and 3D-LiDAR data. By experimentally in-depth analysis, the superiority of the proposed method is validated.
AB - Vision+X has become the promising tendency for scene understanding in autonomous driving, where X may be the other non-vision sensors. However, it is difficult to utilize all the superiority of different sensors, mainly because of the heterogenous, asynchronous properties. To this end, this paper calibrates the commonly used RGB+3D-LiDAR data by synchronization and an online spatial structure alignment, and obtains a high-accurate calibration performance. The main highlights are that (1) we rectify the 3D points with the aid of differential inertial measurement unit (IMU), and increase the frequency of 3D laser data as the same as the ones of RGB data, and (2) this work can online high-accurately updates the external parameters of calibration by a more reliable spatial-structure matching of RGB and 3D-LiDAR data. By experimentally in-depth analysis, the superiority of the proposed method is validated.
UR - https://www.scopus.com/pages/publications/85040255820
U2 - 10.1007/978-3-319-71598-8_23
DO - 10.1007/978-3-319-71598-8_23
M3 - 会议稿件
AN - SCOPUS:85040255820
SN - 9783319715971
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 254
EP - 263
BT - Image and Graphics - 9th International Conference, ICIG 2017, Revised Selected Papers
A2 - Zhao, Yao
A2 - Kong, Xiangwei
A2 - Taubman, David
PB - Springer Verlag
T2 - 9th International Conference on Image and Graphics, ICIG 2017
Y2 - 13 September 2017 through 15 September 2017
ER -