Line feature based self-calibration method for dual-axis scanning LiDAR system

Abstract

LiDAR calibration is an essential step for acquiring accurate three-dimensional point-cloud. To enhance the convenience of calibration of dual-axis scanning LiDAR, a line feature based self-calibration method is proposed, which corrects point-cloud error by establishing observation model and estimating model parameters. The model parameters are estimated by minimizing the square error of line feature points, referring as straight edges extracted from point-cloud. The experimental results verify the effectiveness of the proposed method on improving point-cloud accuracy, the horizontal and vertical angular errors are reduced to 0.10° and 0.08° after calibration with reductions of 64% and 68%, respectively. The analysis on parameter correlation finds that, parameters can be divided into groups according to the introduced point-cloud error: dimension error, trapezoid and parallelogram shape distortion. A de-correlation strategy combining inter-group and stepwise intra-group de-correlation thus can be implemented, which can be applied for point-cloud quality enhancement in in-situ environmental perception scenes.