Pose Estimation of Curved Objects Based on Binocular Vision and Vectors of the Tangent Plane

Yuzhen Liu; Jiarong Zhang; Sen Lin

doi:10.3788/LOP57.041506

Laser & Optoelectronics Progress, Volume. 57, Issue 4, 041506(2020)

Pose Estimation of Curved Objects Based on Binocular Vision and Vectors of the Tangent Plane

Yuzhen Liu¹, Jiarong Zhang^1、*, and Sen Lin^1,2,3

Author Affiliations

¹School of Electronic and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China

²State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China

³Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China

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Figures & Tables(20)

Fig. 1. Binocular vision measurement system model

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Fig. 2. Conversion from depth map to point cloud. (a) Depth map; (b) point cloud

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Fig. 3. Schematic of rotation of 3D coordinate system. (a) Rotate around X axis; (b) rotate around Y axis; (c) rotate around Z axis

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Fig. 4. Position representation of spatial point in world coordinate system

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Fig. 5. Pose transformation of curved object

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Fig. 6. Flow chart of the algorithm

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Fig. 7. Experimental environment

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Fig. 8. Experimental object. (a) Object 1; (b) object 2; (c) object 3; (d) object 4

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Fig. 9. Selected target corner points

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Fig. 10. Comparison of measurement errors of our algorithm and three monocular visual pose algorithms. (a) X-axis translation error; (b) Y-axis translation error; (c) Z-axis translation error; (d) rotation error around the X-axis; (e) rotation error around the Y-axis; (f) rotation error around the Z-axis

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Fig. 11. Average error percentage of our algorithm and three monocular visual pose algorithms

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Fig. 12. Comparison of measurement errors of our algorithm and two binocular visual pose algorithms. (a) X-axis translation error; (b) Y-axis translation error; (c) Z-axis translation error; (d) rotation error around the X-axis; (e) rotation error around the Y-axis; (f) rotation error around the Z-axis

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Fig. 13. Average error percentage of our algorithm and two binocular visual pose algorithms

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Table 1. Estimation results of object 1 pose change

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Table 1. Estimation results of object 1 pose change

Data category	Translation /cm			Rotation angle /(°)
Data category	t_x	t_y	t_z	ϕ	θ	ψ
Ground truth	5.0000	4.5000	10.0000	0.0000	10.0000	10.0000
Estimated value	6.6207	4.0626	11.3933	4.2442	9.9221	10.3074
Estimation error	1.6207	0.4374	1.3933	4.2442	0.0779	0.3074
Average error	1.1505			1.5432

Table 2. Estimation results of object 2 pose change

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Table 2. Estimation results of object 2 pose change

Data category	Translation /cm			Rotation angle /(°)
Data category	t_x	t_y	t_z	ϕ	θ	ψ
Ground truth	10.0000	4.5000	0.0000	5.0000	0.0000	0.0000
Estimated value	11.3664	4.8467	0.7567	4.0535	0.5914	0.7830
Estimation error	1.3664	0.3467	0.7567	0.9465	0.5914	0.7830
Average error	0.8233			0.7736

Table 3. Estimation results of object 3 pose change

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Table 3. Estimation results of object 3 pose change

Data category	Translation /cm			Rotation angle /(°)
Data category	t_x	t_y	t_z	ϕ	θ	ψ
Ground truth	5.0000	0.0000	10.0000	0.0000	20.0000	0.0000
Estimated value	6.1803	0.1977	11.7300	4.7397	19.5883	1.8791
Estimation error	1.1803	0.1977	1.7300	4.7397	0.4117	1.8791
Average error	1.0360			2.3435

Table 4. Estimation results of object 4 pose change

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Table 4. Estimation results of object 4 pose change

Data category	Translation /cm			Rotation angle /(°)
Data category	t_x	t_y	t_z	ϕ	θ	ψ
Ground truth	0.0000	0.0000	10.0000	5.0000	0.0000	0.0000
Estimated value	0.4850	0.0327	10.0533	3.3628	0.8692	2.2322
Estimation error	0.4850	0.0327	0.0533	1.6372	0.8692	2.2322
Average error	0.1903			1.5795

Table 5. Mean estimation error of each object
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Table 5. Mean estimation error of each object
Object Translation /cm Rotation angle /(°)
1 1.1505 1.5432
2 0.8233 0.7736
3 1.0360 2.3435
4 0.1903 1.5795

Table 6. Comparison of calculation efficiency between COPE and ICP algorithm
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Table 6. Comparison of calculation efficiency between COPE and ICP algorithm
Algorithm Running time /ms
o_tx o_ty o_tz o_ϕ o_θ o_ψ
COPE 11.80 8.40 8.20 8.50 8.50 8.75
ICP 595.00 382.20 408.40 536.00 618.00 621.00
Increased percentage /% 98.02 97.80 97.99 98.41 98.62 98.59
Average improved efficiency /% 98.24

Table 7. Comparison of calculation efficiency between COPE and NDT algorithm
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Table 7. Comparison of calculation efficiency between COPE and NDT algorithm
Algorithm Running time /ms
o_tx o_ty o_tz o_ϕ o_θ o_ψ
NDT 210.00 305.00 429.20 614.00 570.00 640.00
Increased percentage /% 94.38 97.25 98.09 98.62 98.51 98.63
Average improved efficiency /% 97.58

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Yuzhen Liu, Jiarong Zhang, Sen Lin. Pose Estimation of Curved Objects Based on Binocular Vision and Vectors of the Tangent Plane[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041506

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