Accurate Calibration of a Self-Developed Vehicle-Borne LiDAR Scanning System

Ming Guo,Deng Pan,Bingnan Yan,Guoli Wang,Tengfei Zhou,Romeo Bernini

doi:10.1155/2021/8816063

Abstract

To obtain high-precision measurement data using vehicle-borne light detection and ranging (LiDAR) scanning (VLS) systems, calibration is necessary before a data acquisition mission. Thus, a novel calibration method based on a homemade target ball is proposed to estimate the system mounting parameters, which refer to the rotational and translational offsets between the LiDAR sensor and inertial measurement unit (IMU) orientation and position. Firstly, the spherical point cloud is fitted into a sphere to extract the coordinates of the centre, and each scan line on the sphere is fitted into a section of the sphere to calculate the distance ratio from the centre to the nearest two sections, and the attitude and trajectory parameters of the centre are calculated by linear interpolation. Then, the real coordinates of the centre of the sphere are calculated by measuring the coordinates of the reflector directly above the target ball with the total station. Finally, three rotation parameters and three translation parameters are calculated by two least-squares adjustments. Comparisons of the point cloud before and after calibration and the calibrated point clouds with the point cloud obtained by the terrestrial laser scanner show that the accuracy significantly improved after calibration. The experiment indicates that the calibration method based on the homemade target ball can effectively improve the accuracy of the point cloud, which can promote VLS development and applications.

Highlights

With the rapid development of information technology, geographic information data and geospatial data are increasingly playing important roles in urban construction and social services, which require faster updating and higher accuracy of data
The quantitative evaluation can reflect the overall accuracy of the VLS system by calculating the relative accuracy and absolute accuracy of the point cloud with the root mean square error (RMSE) formula, and the qualitative evaluation can evaluate the feasibility of calibration by comparing the changes of point clouds before and after calibration
The coordinates of the spherical centre and other feature points measured by the total station as true values were used to calculate the coordinate error, and the mean square error was calculated according to the RMSE formula

Summary

Introduction

With the rapid development of information technology, geographic information data and geospatial data are increasingly playing important roles in urban construction and social services, which require faster updating and higher accuracy of data. As a type of MMS, the VLS system integrates global navigation satellite systems (GNSS), inertial measurement units, LiDAR scanning systems, image acquisition systems, and time synchronization systems on a common vehicle platform [3]. During vehicle driving, it measures the information of roads and buildings on both sides in real time and obtains image data and point cloud data of the measured objects by the camera and LiDAR scanner. Compared with other scanning methods, VLS has the advantages of high efficiency and strong flexibility and has been widely utilized to generate high-quality 3D geospatial information for urban environments [5].

Methods

Results

Conclusion