在低亮度条件下,传统的事故勘察方法难以获取高质量的勘察数据.提出了基于机载激光雷达的低照度事故现场重建方法.首先,建立机载激光雷达勘察的方法框架.接着,使用高斯分布的统计学滤波算法去除噪点,通过判断空间划分体素的占据状态来滤除现场周围移动物体.然后,利用传感器自身的位姿数据配准点云数据,建立事故现场的三维点云模型.此外,探究了无人机飞行高度和激光旁向重叠率如何影响建模精度.最后,在夜间模拟事故现场进行实证研究,研究发现当无人机飞行高度为15 m,激光旁向重叠率为50%时,建模精度和处理时间能达到较好平衡.与航拍摄影建模、传统人工勘察方法相比,机载激光雷达建模均方根误差(root mean square error,RMSE)为0.046 36,低于航拍摄影建模误差,表明方法能够应用于低照度交通事故现场勘测.
Reconstruction of low-brightness traffic accident scene based on UAV-borne LiDAR and empirical research
Under low-brightness conditions,it is difficult to obtain high-quality survey data by traditional accident survey methods.This paper proposes a low-brightness accident scene reconstruction method based on UAV-borne LiDAR.First,the methodological framework of UAV-borne LiDAR survey is established.Then the noise is removed using a statistical filtering algorithm with Gaussian distribution,and the moving objects around the scene are filtered out by judging the occupancy status of the spatially divided voxels.Finally,the sensor's position data is used to registration the point cloud data to obtain a 3D point cloud model of the accident scene.This paper also explores how UAV flight altitude and LiDAR side overlap rate affect the modeling accuracy.An empirical study is conducted at a simulated accident scene at night,and it is found that when the UAV flight height is 15 m and the LiDAR side overlap rate is 50%,the accuracy and processing time can reach a better state.Comparison and analysis with aerial photography modeling and traditional manual survey methods,the root mean square error(RMSE)of airborne LiDAR modeling is 0.046 36,which is lower than that of aerial photography modeling,which can be better applied to the survey of traffic accidents at the scene of low illumination.
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