Impact of Lidar System Calibration on the Relative and Absolute Accuracy of the Adjusted Point Cloud

The availability of 3D surface data is valuable for several industrial, public, and military applications. Light Detection And Ranging (LiDAR) is an active sensor system capable of collecting 3D information from an object surface using directly geo-referenced laser pulses. Accurate and dense LiDAR data can be utilized for photogrammetric data geo-referencing and segmentation of 3D buildings. LiDAR data contaminated by systematic errors (e.g., biases in the mounting parameters) cannot guarantee the achievement of the expected accuracy and discrepancies might occur between overlapping strips. This paper introduces an alternative method for LiDAR system calibration. In the proposed method, biases in LiDAR system parameters are estimated using time-tagged point cloud and trajectory data (position only). Unlike conventional calibration methods, the proposed method does not require raw measurements such as GPS/INS observations, mirror scan angles, and ranges for the laser footprints. Biases in the system mounting parameters are estimated while reducing discrepancies between conjugate surface elements in overlapping strips. Estimated biases are then used to adjust the point cloud. The influence of LiDAR system calibration is analyzed through the evaluation of the relative and absolute accuracy before/after the calibration. The evaluation of the relative accuracy will be based on quantifying the degree of compatibility between conjugate surface elements in overlapping strips before and after the calibration procedure. In addition, the impact of the LiDAR system calibration on the absolute accuracy of the point cloud is evaluated by using the LiDAR data for photogrammetric georeferencing before and after performing the proposed calibration procedure. The outcome of the photogrammetric reconstruction will be evaluated through check point analysis. The experimental results have shown that the proposed calibration procedure improves the relative and absolute accuracy of the LiDAR point cloud. * Corresponding author.