A Point Cloud Splicing Method of Rectangular Fragment Interception Target Based on Euclidean Space Transformation

doi:10.12382/bgxb.2024.0178

Abstract

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

CLC Number:

TJ410.6

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-.

Figures/Tables 19

Fig.1 Time of flight method

Fig.2 Acquisition of target point cloud by laser scanning

Fig.3 Point clouds of rectangular fragment interception target

Fig.4 Rotation angles αi and γi

Fig.5 Rotation angle βi

Fig.6 Effect of adjusting the angular posture

Fig.7 Effect of overall splicing

Fig.8 Flowchart of target point cloud data processing

Fig.9 Rectangular fragment interception target

Fig.10 The position of warhead and fragment interception target

Fig.11 Acquisition of target point clouds by laser scanner

Fig.12 Local target point clouds

Fig.13 Angular posture of local target point clouds

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)

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)

Fig.14 Rotation transformation

Fig.15 Overall splicing effect of target array

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

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

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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