Road-Segmentation based Curb Detection Method for Self-driving via a 3D-LiDAR Sensor

Yihuan Zhang, Jun Wang, Xiaonian Wang and John M. Dolan
Journal Article, IEEE Transactions on Intelligent Transportation Systems, Vol. 19, No. 12, pp. 3981-3991, December, 2018

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The effective detection of curbs is fundamental and crucial for the navigation of a self-driving car. This paper presents a real-time curb detection method that automatically segments the road and detects its curbs using a 3D-LiDAR sensor. The point cloud data of the sensor are first processed to distinguish on-road and off-road areas. A sliding-beam method is then proposed to segment the road by using the off-road data. A curb-detection method is finally applied to obtain the position of curbs for each road segments. The proposed method is tested on the data sets acquired from the self-driving car of Laboratory of VeCaN at Tongji University. Off-line experiments demonstrate the accuracy and robustness of the proposed method, i.e., the average recall, precision and their harmonic mean are all over 80%. Online experiments demonstrate the real-time capability for autonomous driving as the average processing time for each frame is only around 12 ms.

author = {Yihuan Zhang and Jun Wang and Xiaonian Wang and John M. Dolan},
title = {Road-Segmentation based Curb Detection Method for Self-driving via a 3D-LiDAR Sensor},
journal = {IEEE Transactions on Intelligent Transportation Systems},
year = {2018},
month = {December},
volume = {19},
number = {12},
pages = {3981-3991},
keywords = {self-driving, 3D-LiDAR sensor, sliding-beam model, road segmentation, curb detection},
} 2019-08-15T14:59:24-04:00