摘要
激光雷达在作业前需要进行准确的标定,否则很容易导致拍摄出来的图像发生畸变。为保证拍摄质量,针对传统标定方法精度不足的问题,研究一种无人机空中激光雷达拍摄图像目标自标定方法。针对机载激光雷达拍摄到的目标点云图像实施去噪处理。利用深度学习中的KCRNet网络构建一种点云匹配模型,实现特征点云匹配。以特征点对为基础,构建标定参数优化模型。利用遗传算法对模型进行求解,得出最优标定参数,完成无人机空中激光雷达拍摄图像目标自标定。结果表明:所研究方法标定后,粗差率相对更小,最高值仅为2.6%,标定精度均值为99.2%,标定时间均值仅为5.0 s,由此说明该方法的标定效果更好,使得拍摄出来的图像质量更高。
Laser radar requires accurate calibration before operation,otherwise it can easily cause distortion in the captured images.To ensure the quality of shooting and address the issue of insufficient accuracy in traditional calibration methods,a target self calibration method for unmanned aerial vehicle(UAV)airborne LiDAR imaging is studied.Implement denoising processing for target point cloud images captured by airborne LiDAR.Construct a point cloud matching model using the KCRNet network in deep learning to achieve feature point cloud matching.Based on feature point pairs,construct a calibration parameter optimization model.Using genetic algorithm to solve the model,obtain the optimal calibration parameters,and complete the target self calibration of unmanned aerial vehicle aerial LiDAR images.The results show that after calibration,the gross error rate is relatively smaller,the highest value is only 2.6%,the average calibration accuracy is 99.2%,and the average calibration time is only 5.0s,which indicates that the calibration effect of the method is better and the image quality is higher.
作者
王大鹏
WANG Dapeng(Kede College of Capital Normal University,Beijing 102602,China)
出处
《激光杂志》
CAS
北大核心
2024年第9期171-176,共6页
Laser Journal
基金
北京市自然科学基金青年项目(No.4194087)。
关键词
无人机
激光雷达
点云匹配
参数优化模型
自标定方法
drones
LiDAR
point cloud matching
parameter optimization model
self calibration method
作者简介
王大鹏(1975-)男,副教授,博士,研究方向:无人机航拍。E-mail:wangdapeng0@126.com。
