光学精密工程2024，Vol.32Issue(19)：2945-2956,12.DOI:10.37188/OPE.20243219.2945
基于影像视锥的二维三维一体雷视融合车速测量
Integrated 2D-3D LiDAR-vision fusion vehicle speed estimation based on image frustum
摘要
Abstract
This paper introduces an image-priority approach with integrated 2D-3D views for continuous multi-target tracking and speed estimation in camera-LiDAR surveillance systems.It addresses differences in acquisition frequency,resolution,and viewing angles between camera and LiDAR-based speed estima-tion tasks.Geometrically matched feature points are selected,and external parameters are computed using the Direct Linear Transformation method for online calibration between devices.A vision-guided,frus-tum-based spatial method combines 2D and 3D localization,uses high-resolution ground points to define area boundaries,and adapts clustering parameters in a 2D top-down view transformed from a 3D frustum perspective.This approach helps eliminate irrelevant points and address mixed-resolution point cloud de-tection due to varying viewing angles.The speed estimation process employs Kalman filtering and vehicle motion states,modeling speed estimation as observation equations using discrete synchronized frame point cloud data.The observation noise covariance matrix is calculated based on point cloud resolution,allow-ing continuous optimal estimation and reducing observation noise and asynchronous timing effects.Experi-ments on traffic scene datasets show that the method achieves an average absolute error of 0.276 4 m/s and a root mean square error of 0.325 1 m/s,with a maximum detection range of 103.211 m,demonstrat-ing high accuracy and practicality.关键词
智能交通/速度测量/影像视锥/数据融合/卡尔曼滤波/目标检测Key words
intelligent transportation/speed estimation/image frustum/data fusion/Kalman filtering/object detection分类
计算机与自动化引用本文复制引用
周奎宇,黄玉春,杨鹤,李娜..基于影像视锥的二维三维一体雷视融合车速测量[J].光学精密工程,2024,32(19):2945-2956,12.基金项目
国家自然科学基金资助项目(No.41671419) （No.41671419）
河南省交通运输厅科技计划项目(No.2022-3-2) （No.2022-3-2）
