Detection of road roughness and drivable area based on LiDAR

YAN Deli; GAO Shang; LI Shaohua; HUO Meng

doi:10.7510/jgjs.issn.1001-3806.2022.05.007

LASER TECHNOLOGY > 2022 > 46(5): 624-629. > DOI: 10.7510/jgjs.issn.1001-3806.2022.05.007

YAN Deli, GAO Shang, LI Shaohua, HUO Meng. Detection of road roughness and drivable area based on LiDAR[J]. LASER TECHNOLOGY, 2022, 46(5): 624-629. DOI: 10.7510/jgjs.issn.1001-3806.2022.05.007

Citation:

YAN Deli, GAO Shang, LI Shaohua, HUO Meng. Detection of road roughness and drivable area based on LiDAR[J]. LASER TECHNOLOGY, 2022, 46(5): 624-629. DOI: 10.7510/jgjs.issn.1001-3806.2022.05.007

Citation:

YAN Deli, GAO Shang, LI Shaohua, HUO Meng. Detection of road roughness and drivable area based on LiDAR[J]. LASER TECHNOLOGY, 2022, 46(5): 624-629. DOI: 10.7510/jgjs.issn.1001-3806.2022.05.007

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Detection of road roughness and drivable area based on LiDAR

1.
State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
2.
School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
3.
Hebei Province Center for Disease Prevention and Control, Shijiazhuang 050021, China

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Received Date: August 17, 2021
Revised Date: October 08, 2021
Published Date: September 24, 2022

Graphical Abstract

Abstract

Abstract

In order to improve the accuracy of road unevenness detection by vehicle-mounted lidar in outdoor scenes, the road environment information was extracted and segmented by the network structure of random down-sampling and local feature aggregation. Random sampling method was added in the segmentation process to improve the computing efficiency of high point cloud information. To solve the problem of the loss of key features in the segmentation process of road environment information, local feature aggregator was added to increase the acceptance domain of each 3-D point cloud to retain geometric details. The results show that the proposed algorithm can accurately identify the road environment information, and the recognition accuracy of convex hull, pit, and road able area reaches 71.87%, 82.71%, and 93.01% respectively, which is significantly improved compared with the traditional convolution neural network. This study can efficiently extract the information of road roughness and road able area. Thus, the active safety and ride comfort of the vehicle are improved.
- laser technique,
- random down sampling,
- local feature aggregation,
- point cloud

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References (20)

References

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