一种基于激光雷达点云的自适应双半径滤波算法

doi:10.12382/bgxb.2022.1093

摘要/Abstract

摘要：

点云去噪技术是智能驾驶汽车感知周边环境信息的关键一步。针对激光雷达点云去噪算法去噪精度高、运行速率低的问题,提出一种适用于复杂场景和多种尺度噪声下的自适应双半径滤波算法。三维点云经最少点数约束条件下的体素滤波精简处理,并初步滤除离群噪声。用KD-tree建立索引计算点云的平均密度。根据点云密度构建自适应大、小半径模型,以滤除漂移噪声体素。为验证算法的有效性,在多噪声类型的简单场景和复杂场景下,与各算法对比去噪精度与运行速率。对比结果表明,在去噪精度略微降低的情况下,在简单场景中的运行时间低于0.6s,在复杂场景中低于2s,新算法具有较高的去噪精度和运行速率及较广的适用范围。

关键词: 激光雷达三维点云, 点云去噪, 自适应滤波, 双半径滤波

Abstract:

Point cloud denoising is a key step for intelligent driving vehicles to perceive the surrounding environment information. To solve the problem of low operation speed of high-precision denoising in the LiDAR point cloud denoising method, a self-adaptive dual radius filtering method is proposed for complex scenes and multi-scale noise. The 3D point cloud is first simplified by voxel filtering under the constraint of the minimum number of points, and the outliers are preliminarily filtered. Then KD-tree is used to build an index to calculate the average density of point clouds.The adaptive large- and small-radius models are constructed according to the point cloud density to filter drift noise voxels. To verify the effectiveness of the algorithm, in the simple and complex scenes with multiple noise types, the noise removal accuracy and operation speed are compared with other algorithms. In the case of slightly reduced noise removal accuracy, the operation time is less than 0.6 seconds in simple scenes and less than 2 seconds in complex scenes. The new algorithm has high noise removal accuracy and operation speed, as well as a wide range of applications.

Key words: LiDAR point cloud, point cloud denoising, adaptive filtering, dual radius filtering

中图分类号:

TP391.9

柳斌, 李雪梅. 一种基于激光雷达点云的自适应双半径滤波算法[J]. 兵工学报, 2023, 44(9): 2768-2777.

LIU Bin, LI Xuemei. A Self-adaptive Dual Radius Filtering Algorithm Based on LiDAR Point Cloud[J]. Acta Armamentarii, 2023, 44(9): 2768-2777.

图/表 10

图1 半径滤波计算

Fig.1 Radius filtering calculation

图2 去噪方案流程

Fig.2 Denoising scheme process

图3 自适应双半径滤波程序流程

Fig.3 Self-adaptive dual radius filtering program flow

图4 Bunny模型下各滤波算法可视化图

Fig.4 Visualization of each filtering algorithm for the Bunny model

图5 Road场景下各滤波算法可视化图

Fig.5 Visualization of each filtering algorithm for the Road scene

表1 各滤波算法参数设置

Table 1 Parameter setting of each filtering algorithm

滤波算法	参数	Bunny模型	Road场景	滤波算法	参数	Bunny模型	Road场景
体素滤波	格子体积V_g/cm³	0.2×0.2×0.2	12×12×12		V_g/cm³	0.2×0.2×0.2	10×10×10
半径滤波	R/cm	0.15	30		M_P	2	1
	R_T	1	10	自适应双半径滤波	M_L	30	40
中值滤波	N_mf	5	5		N_L	1	5
统计滤波	N_sf	20	20		M_S	40	60
	k	0.5	0.5		N_S	1	4

图6 去噪精确率对比(横轴刻度已省略重复的噪声均值)

Fig.6 Comparison of denoising accuracy (The horizontal scale has comitted duplicate noise mean values)

图7 噪声召回率对比(横轴刻度已省略重复的噪声均值)

Fig.7 Comparison of noise recall rate (The horizontal scale has comitted duplicate noise mean values)

图8 原点保留率对比(横轴刻度已省略重复的噪声均值)

Fig.8 Comparison of original poretention rate (The horizontal scale has comitted duplicate noise mean values)

图9 运行时间对比(横轴刻度已省略重复的噪声均值)

Fig.9 Comparison of runtime (The horizontal scale has comitted duplicate noise mean values)

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