Automatic Calibration Method for Airborne LiDAR Systems Based on Approximate Corresponding Points Model

Yu Tian,Chuning Ji,Shaogang Lei,Yibo Zhao,Lei Duan,Vladimír Sedlák,Bin Gao

doi:10.1155/2022/4853419

Abstract

The calibration of the light detection and ranging (LiDAR) system is critical to ensure the accuracy of point data. In this paper, the lever-arm measurement of airborne LiDAR system (ALS) was realized by photogrammetry. An automatic iterative boresight calibration method based on approximate corresponding points (CPs) matching was proposed to correct the boresight misalignment. It was based on iterative closest point (ICP) registration algorithm with a normal space sampling strategy, and approximate CPs were obtained by establishing filter rules. The experimental results showed that the absolute accuracy of the calibrated ALS reached 7.13 cm when the flight altitude was 100 m, meeting the accuracy requirements.

Highlights

airborne light detection and ranging (LiDAR) system (ALS) is widely used in topographic mapping, digital city, power line inspection, and other fields
We adopted three steps to improve the accuracy of iterative closest point (ICP) alignment: first, after removing the outliers, the point cloud was divided into small blocks to ensure the consistency between the source point cloud and the target point cloud through a rigid transformation; second, the feature points were selected by uniform sampling in normal vector space
The uncertainty of GPCs combined with the uncertainty in the intrinsic modelling of the SfM was translated into uncertainty in the 3D model

Summary

Introduction

ALS is widely used in topographic mapping, digital city, power line inspection, and other fields. A laser scanner, a global position system (GPS), and an inertial navigation system (INS) were integrated into ALS. It calculates the distance between the scanner and the target by the time interval from transmitting laser pulse to receiving echo signal. Boresight misalignment and lever-arm offsets are the primary error sources that bias the LiDAR point cloud positioning [4]. Their presence causes noncoincidence and misalignment between LiDAR point cloud strips, which seriously affects the overall accuracy and precision of the data [5, 6]. High-precision geospatial applications of UAVs especially require calibration of the boresight misalignment and lever-arm offsets [7]

Methods

Results

Conclusion