基于欧式变换的矩形破片拦截靶点云拼接方法

doi:10.12382/bgxb.2024.0178

摘要/Abstract

摘要：

三维激光扫描技术可直接获取破片拦截靶板的高精度点云数据,从而识别提取破片对拦截靶形成的孔洞、凹坑等损伤特征,然而对于大规模靶板阵列,激光扫描仪在多个位置、角度所采集的靶板局部点云位置相互独立,难以反映靶板阵列整体结构,为此提出一种基于欧式空间变换的矩形靶板阵列点云拼接方法。根据局部点云角点坐标及位置关系构建旋转矩阵与平移向量,通过多次旋转变换与平移变换实现多个局部点云的角度姿态调整,拼接为一个矩形破片拦截靶整体点云。与现场靶板阵列的尺寸相对比,拼接所得靶板整体点云的高度与长度的平均相对误差分别为2.035%、1.192%。所提方法填补了破片飞散分布测试技术领域靶板阵列激光点云拼接方法的研究空白,在此基础上未来可结合破片特征识别技术,进一步开展基于激光点云的战斗部破片场飞散分布三维重构方面的研究。

关键词: 破片分布测试, 点云数据处理, 欧式空间变换, 旋转矩阵, 平移向量

Abstract:

Three-dimensional laser scanning technology can be used to acquire the high-precision point cloud data of fragment interception target directly.The damage characteristics such as holes and pits formed by fragments on the target can be identified and extracted.However,the positions of local point clouds collected by the laser scanner at multiple positions and angles are independent of each other,making it difficult to reflect the overall structure of a large-scale target array.A point cloud splicing method of rectangular fragment interception target based on Euclidean space transformation is proposed.The rotation matrix and translation vector are constructed based on the corner coordinates and position relationships of local point clouds.Through multiple rotation and translation transformations,the angle and posture of multiple local point clouds are adjusted to splice them into an overall point cloud of the rectangular fragment interception target.Compared with the size of the target array,the average relative errors of the height and length of overall point cloud acquired by this splicing method are 2.035% and 1.192%,respectively.This study fills the research gap of target array laser point cloud splicing method in the field of fragment dispersion distribution testing technology.On this basis,the 3D reconstruction of fragment field dispersion distribution of warheads based on laser point clouds can be studied with fragment feature recognition technology in the future.

Key words: fragment distribution test, point cloud data processing, Euclidean space transformation, rotation matrix, translation vector

中图分类号:

TJ410.6

任杰, 蒋海燕, 姬建荣. 基于欧式变换的矩形破片拦截靶点云拼接方法[J]. 兵工学报, 2025, 46(2): 240178-.

REN Jie, JIANG Haiyan, JI Jianrong. A Point Cloud Splicing Method of Rectangular Fragment Interception Target Based on Euclidean Space Transformation[J]. Acta Armamentarii, 2025, 46(2): 240178-.

图/表 19

图1 飞行时间法示意图

Fig.1 Time of flight method

图2 激光扫描采集靶板点云示意图

Fig.2 Acquisition of target point cloud by laser scanning

图3 矩形破片拦截靶点云

Fig.3 Point clouds of rectangular fragment interception target

图4 旋转角度αi、γi

Fig.4 Rotation angles αi and γi

图5 旋转角度βi

Fig.5 Rotation angle βi

图6 角度姿态调整效果

Fig.6 Effect of adjusting the angular posture

图7 整体拼接效果

Fig.7 Effect of overall splicing

图8 靶板点云数据处理流程

Fig.8 Flowchart of target point cloud data processing

图9 试验现场矩形破片拦截靶

Fig.9 Rectangular fragment interception target

图10 战斗部与破片拦截靶位置

Fig.10 The position of warhead and fragment interception target

图11 激光扫描仪采集点云数据

Fig.11 Acquisition of target point clouds by laser scanner

图12 靶板局部区域点云

Fig.12 Local target point clouds

图13 靶板各局部点云的角度姿态

Fig.13 Angular posture of local target point clouds

表1 局部点云数据计算参数

Table 1 Point cloud data calculation parameters

i	F_i/m	G_i/m	I_i/m	n_i/m	α_i/rad	γ_i/rad	β_i/rad	R_i	t_i/m
1	(6.213, 5.306, 151.789)	(8.823, -4.303, 152.273)	(8.400, -4.603, 149.360)	(28.136, 7.398, -4.848)	-0.580	1.314	-0.510	$- 0.967 - 0.213 0.139 0.222 - 0.974 0.054 0.124 0.084 0.989$	(-25.671, 0, 0)
2	(6.952, -2.174, 152.653)	(-5.012, -2.960, 153.996)	(-5.268, -3.813, 151.219)	(3.329, -33.571, 10.007)	-0.290	-0.099	0.088	$- 0.991 - 0.069 0.113 0.099 - 0.954 0.284 0.088 0.293 0.952$	(-22.500, -31.720, 5.978)
3	(3.901, -6.698, 153.523)	(-0.098, 4.301, 154.942)	(-0.637, 4.433, 152.113)	(-31.303, -12.078, 5.402)	-0.421	1.203	0.513	$- 0.314 0.942 0.116 - 0.933 - 0.329 0.147 0.177 - 0.062 0.982$	(-10.326, -6.694, -0.615)
4	(1.365, -6.849, 156.680)	(-5.528, -3.143, 156.741)	(-5.417, -2.876, 153.757)	(-11.075, -20.560, -2.249)	0.109	0.494	-0.044	$- 0.474 - 0.875 - 0.096 0.880 - 0.476 - 0.008 - 0.039 - 0.088 0.995$	(21.435, 11.228, -5.211)

表1 局部点云数据计算参数

Table 1 Point cloud data calculation parameters

i	F_i/m	G_i/m	I_i/m	n_i/m	α_i/rad	γ_i/rad	β_i/rad	R_i	t_i/m
1	(6.213, 5.306, 151.789)	(8.823, -4.303, 152.273)	(8.400, -4.603, 149.360)	(28.136, 7.398, -4.848)	-0.580	1.314	-0.510	$- 0.967 - 0.213 0.139 0.222 - 0.974 0.054 0.124 0.084 0.989$	(-25.671, 0, 0)
2	(6.952, -2.174, 152.653)	(-5.012, -2.960, 153.996)	(-5.268, -3.813, 151.219)	(3.329, -33.571, 10.007)	-0.290	-0.099	0.088	$- 0.991 - 0.069 0.113 0.099 - 0.954 0.284 0.088 0.293 0.952$	(-22.500, -31.720, 5.978)
3	(3.901, -6.698, 153.523)	(-0.098, 4.301, 154.942)	(-0.637, 4.433, 152.113)	(-31.303, -12.078, 5.402)	-0.421	1.203	0.513	$- 0.314 0.942 0.116 - 0.933 - 0.329 0.147 0.177 - 0.062 0.982$	(-10.326, -6.694, -0.615)
4	(1.365, -6.849, 156.680)	(-5.528, -3.143, 156.741)	(-5.417, -2.876, 153.757)	(-11.075, -20.560, -2.249)	0.109	0.494	-0.044	$- 0.474 - 0.875 - 0.096 0.880 - 0.476 - 0.008 - 0.039 - 0.088 0.995$	(21.435, 11.228, -5.211)

图14 旋转变换

Fig.14 Rotation transformation

图15 靶板阵列整体点云拼接效果

Fig.15 Overall splicing effect of target array

表2 局部点云欧式变换用时

Table 2 Time of local point cloud euclidean transformation

i	包含点数	欧式变换用时/s
1	6638258	0.180
2	10927116	0.297
3	9849981	0.292
4	7042787	0.242

表3 靶板点云拼接前尺寸计算参数

Table 3 Calculation parameters about the size of target point cloud before splicing

i	F_i/m	G_i/m	I_i/m	高度/m	长度/m
1	(6.213,5.306,151.789)	(8.823,-4.303,152.273)	(8.400,-4.603,149.360)	2.959	9.969
2	(6.952,-2.174,152.653)	(-5.012,-2.960,153.996)	(-5.268,-3.813,151.219)	2.917	12.065
3	(3.901,-6.698,153.523)	(-0.098,4.301,154.942)	(-0.637,4.433,152.113)	2.883	11.789
4	(1.365,-6.849,156.680)	(-5.528,-3.143,156.741)	(-5.417,-2.876,153.757)	2.998	7.827

表4 靶板点云拼接后尺寸计算参数表

Table 4 Calculation parameters about the size of target point cloud after splicing

i	F″_i/m	G″_i/m	I″_i/m	高度/m	长度/m	高度相对误差/%	长度相对误差/%
1	(-11.652,4.471,151.295)	(-12.065,14.432,151.295)	(-11.998,14.472,148.337)	2.959	9.969	1.376	0.308
2	(-12.065,14.432,151.295)	(0,14.383,151.295)	(0,14.382,148.378)	2.917	12.065	2.779	0.539
3	(0,14.432,151.295)	(11.785,14.757,151.295)	(11.750,14.801,148.412)	2.883	11.789	3.905	1.757
4	(11.785,14.432,151.295)	(11.804,6.605,151.295)	(11.804,6.600,148.297)	2.998	7.827	0.081	2.165

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