Journal
SENSORS
Volume 23, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/s23177542
Keywords
LiDAR; angle calibration; Euler angles; point cloud; Kalman filter; RANSAC; vehicle trajectory; ITS
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This paper proposes an adaptive calibration method to address accuracy issues in LiDAR sensors and enable multi-sensor fusion. By comparing two coordinate systems and calculating Euler angles, the method uses rotation matrices to calibrate the point cloud. Experimental results demonstrate that the proposed method achieves high precision.
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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