Citation

Li K, Shao J, Guo D. Sensors (Basel) 2019; 19(7): s19071551.

Affiliation

School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, Shandong 255000, China. likai_academic@163.com.

Copyright

(Copyright © 2019, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s19071551

PMID

30935070

Abstract

In order to improve the accuracy of structured road boundary detection and solve the problem of the poor robustness of single feature boundary extraction, this paper proposes a multi-feature road boundary detection algorithm based on HDL-32E LIDAR. According to the road environment and sensor information, the former scenic cloud data is extracted, and the primary and secondary search windows are set according to the road geometric features and the point cloud spatial distribution features. In the search process, we propose the concept of the largest and smallest cluster points set and a two-way search method. Finally, the quadratic curve model is used to fit the road boundary. In the actual road test in the campus road, the accuracy of the linear boundary detection is 97.54%, the accuracy of the curve boundary detection is 92.56%, and the average detection period is 41.8 ms. In addition, the algorithm is still robust in a typical complex road environment.

Language: en

Keywords

LIDAR point cloud; boundary detection; multi-feature extraction; structured road