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

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%.

Metric Reliability Analysis of Autonomous Marine LiDAR Systems under Extreme Wind Loads

“…Static measurement accuracy is a fundamental guarantee of LiDAR systems, and extensive work has been performed on high-precision calibration and measurement data denoising for LiDAR. In terms of calibration methods, Xin Wen et al [12] proposed an adaptive calibration method to overcome accuracy issues arising from sensor misalignment, and the point cloud matching error was reduced to less than 1.7%, thereby providing more accurate input for subsequent data processing. To improve the localization accuracy of LiDAR point clouds, Roberto Canavosio-Zuzelski et al [13] established a raised hexagonal retro-reflective LiDAR ground target (HRRT) and its measurement model.…”

“…The overall simulation procedure and configuration are shown in Figure 14. First, during the wind loads establishment process, n = 10 4 sets of the wind speeds are sampled from the probability distribution of the annual maximum wind speed shown in Figure 11, and then the wind loads are calculated according to Equation (12).…”

Adaptive Point-Line Fusion: A Targetless LiDAR–Camera Calibration Method with Scheme Selection for Autonomous Driving

“…The calibration has been formulated as a LiDAR Bundle Adjustment problem. Wen [ 16 ] proposed a method for adaptive calibration of LiDAR sensors used on roadsides. The method uses Kalman filtering and the RANSAC algorithm to align the LiDAR coordinate system with an ideal coordinate system.…”

Placement Method of Multiple Lidars for Roadside Infrastructure in Urban Environments

“…Lidar, especially the time-of-flight (TOF) variant, is widely used in autonomous vehicles to acquire point cloud data, aiding in object detection, localization, and map construction. Integrating lidar into roadside infrastructure is emerging, and some studies have been conducted using roadside lidar [1][2][3][4][5][6][7][8][9][10].…”