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Research on an Adaptive Method for the Angle Calibration of Roadside LiDAR Point Clouds.

Authors
  • Wen, Xin1
  • Hu, Jiazun1
  • Chen, Haiyu1
  • Huang, Shichun1
  • Hu, Haonan1
  • Zhang, Hui1
  • 1 School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China. , (China)
Type
Published Article
Journal
Sensors
Publisher
MDPI AG
Publication Date
Aug 30, 2023
Volume
23
Issue
17
Identifiers
DOI: 10.3390/s23177542
PMID: 37687998
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Light Detection and Ranging (LiDAR), a laser-based technology for environmental perception, finds extensive applications in intelligent transportation. Deployed on roadsides, it provides real-time global traffic data, supporting road safety and research. To overcome accuracy issues arising from sensor misalignment and to facilitate multi-sensor fusion, this paper proposes an adaptive calibration method. The method defines an ideal coordinate system with the road's forward direction as the X-axis and the intersection line between the vertical plane of the X-axis and the road surface plane as the Y-axis. This method utilizes the Kalman filter (KF) for trajectory smoothing and employs the random sample consensus (RANSAC) algorithm for ground fitting, obtaining the projection of the ideal coordinate system within the LiDAR system coordinate system. By comparing the two coordinate systems and calculating Euler angles, the point cloud is angle-calibrated using rotation matrices. Based on measured data from roadside LiDAR, this paper validates the calibration method. The experimental results demonstrate that the proposed method achieves high precision, with calculated Euler angle errors consistently below 1.7%.

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