An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data
Abstract
:1. Introduction
- (1)
- For the inspection process of vehicle body parts, an analytical workflow based on point cloud data is established, which is an effective method for the inspection of vehicle body parts.
- (2)
- For the denoising process of the PCD of vehicle body parts, a hybrid denoising algorithm based on straight-through filtering and statistical filtering is proposed, which can extract the target point cloud data accurately.
- (3)
- For the simplification process of PCD, a point cloud subsampling method based on the Fuzzy C-Means (FCM) algorithm is designed, which can keep the vehicle part features while simplifying the point cloud.
- (4)
- For the fine registration process of PCD, the Teaching-Learning-based Optimization (TLBO) algorithm is applied to solve the mathematical model based on the Iterative Closest Point (ICP) algorithm, which can further improve the precision of the fine registration.
2. Related Work
2.1. Preprocessing of Point Clouds
2.2. Parts Inspection
3. Materials and Methods
3.1. Materials
3.2. Methods
3.2.1. The Scheme Proposed
- (1)
- A new hybrid filtering algorithm is proposed for point cloud denoising.
- (2)
- A new point cloud simplification algorithm is proposed to reduce the amount of point cloud data.
- (3)
- A new point cloud registration strategy is used to the measurement point cloud in the vehicle coordinate system.
- (4)
- The K-Nearest Neighbor (KNN) algorithm performs point cloud error inspection, which is also applied to the above three computing processes.
3.2.2. Denoising of PCD Based on a Hybrid Filtering Algorithm
Algorithm 1 Pseudocode of hybrid denoising algorithm |
Begin 1. Input; 2. Initialization Parameter k,, ,,,, , ; 3. Denoise point data according to Equation (1); Output ; 4. Create k-d tree For i = 1 to Build all point k-d tree; End; 5. Creat KNN For i = 1 to Build all point kd-tree; End; 6. Statistical filter For i = 1 to Calculate point KNN mean distance according to Equations (2) and (3); Calculate mean value and standard deviation of all according to Equations (4) and (5); Determine whether point is a noise point according to the Equation (6); End; Output ; End |
3.2.3. Simplification of PCD Based on FCM
Normal Vector Estimation of PCD
Curvature Estimation of the PCD
Point Cloud Density Calculation
Feature Retention Point Cloud Simplification Algorithm Based on Fuzzy C-Means (FCM)
Algorithm 2 Pseudocode of Simplification Algorithm |
Begin Input; Define parameterk, C, m,, T; Fori = 1 to n Calculated the normal vector by PCA as Equations (7) and (8); Correction point cloud normal vector as Equation (9); Build the new point cloud ; For i = 1 to n Calculated the Mean curvature and point density as Equations (10)–(12), respectively; Build the new point cloud ; End Segment the point cloud data by FCM as Equations (13) to (19); Simplify point cloud data as bounding box algorithm and Equation (20); Output the simplified point cloud data. End End |
3.2.4. Registration of PCD
Coarse Registration of PCD
Fine Registration of the PCD
Algorithm 3 Pseudocode of fine registration |
Begin Input data and Initialize U, L, D, T, N Generate initial solutions randomly While t < T do For i =1 to N If f() < f() End if xa,b = random select(solutions) If f() < f() End if End for End while Output |
4. Results and Discussion
4.1. Denoising Experiment
4.2. Simplification Experiment
4.3. Registration Experiment
4.4. Inspection Results
5. Conclusions and Future Work
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
References
- Shu, Z. The Industry 4.0 and Intelligent Manufacturing. Mach. Des. Manuf. Eng. 2013, 43, 1–5. [Google Scholar]
- Guo, J. Study on Production Process Quality Control and Evaluation in Vehicle Manufacturing Enterprise. Ph.D. Thesis, Wuhan University of Technology, Wuhan, China, 2012. [Google Scholar]
- Liu, T. Research on Key Technologies for Automated Measurement of Automotive Comples Parts. Ph.D. Thesis, Tianjin University, Tianjin, China, 2018. [Google Scholar]
- Tran, T.T.; Ha, C.K. Non-contact Gap and Flush Measurement Using Monocular Structured Multi-line Light Vision for Vehicle Assembly. Int. J. Control Autom. Syst. 2018, 16, 2432–2445. [Google Scholar] [CrossRef]
- Fleishman, S.; Drori, I.; Cohen-Or, D. Bilateral mesh denoising. ACM Trans. Graph. 2003, 22, 950–953. [Google Scholar] [CrossRef]
- Fleishman, S.; Cohen-Or, D.; Silva, C. Robust moving least-squares fitting with sharp features. ACM Trans. Graph. 2005, 24, 544–552. [Google Scholar] [CrossRef]
- Gu, X.Y.; Liu, Y.S.; Wu, Q. Research on a Denoising Smoothing Algorithm for 3d Scattered Point Cloud. ICIC Express Lett. 2014, 8, 2403–2409. [Google Scholar]
- Hermosilla, P.; Ritschel, T.; Ropinski, T. Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Seoul, Korea, 27 October 2019; pp. 52–60. [Google Scholar]
- Liu, Z.; Xiao, X.W.; Zhong, S.S.; Wang, W.N.; Li, Y.L.; Zhang, L.; Xie, Z. A feature-preserving framework for point cloud denoising. Comput. Aided Des. 2020, 127, 102857. [Google Scholar] [CrossRef]
- Zhou, S.T.; Liu, X.L.; Wang, C.Y.; Yang, B. Non-iterative denoising algorithm based on a dual threshold for a 3D point cloud. Opt. Laser. Eng. 2020, 126, 105921. [Google Scholar] [CrossRef]
- Chen, Y.; Ng, C.; Wang, Y. Data reduction in integrated reverse engineering and rapid prototyping. Int. J. Comput. Integr. Manuf. 1999, 12, 97–103. [Google Scholar] [CrossRef]
- Lee, K.H.; Woo, H.; Suk, T. Point Data Reduction Using 3D Grids. Int. J. Adv. Manuf. Technol. 2001, 18, 201–210. [Google Scholar] [CrossRef]
- Dyn, N.; Floater, M.S.; Iske, A. Adaptive thinning for bivariate scattered data. J. Comput. Appl. Math. 2002, 145, 505–517. [Google Scholar] [CrossRef] [Green Version]
- Han, H.; Han, X.; Sun, F.; Huang, C. Point cloud simplification with preserved edge based on normal vector. Opt. Int. J. Light Electron Opt. 2015, 126, 2157–2162. [Google Scholar] [CrossRef]
- Qing, S.; Tao, X.; Tatsuo, Y.; Yujie, Z.; Wenting, Y.; Hang, Z. Point cloud simplification algorithm based on particle swarm optimization for online measurement of stored bulk grain. Int. J. Agric. Biol. Eng. 2016, 9, 71–78. [Google Scholar]
- Senin, N.; Colosimo, B.M.; Pacella, M. Point set augmentation through fitting for enhanced ICP registration of; point clouds in multisensor coordinate metrology. Robot. Comput. Integr. Manuf. 2013, 29, 39–52. [Google Scholar] [CrossRef]
- Huang, J.; Wang, Z.; Gao, J.; Huang, Y.; Towers, D.P. High-Precision Registration of Point Clouds Based on Sphere Feature Constraints. Sensors 2016, 17, 72. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Du, Q. 3D point cloud registration denoising method for human motion image using deep learning algorithm. Multimed. Syst. 2019, 26, 75–82. [Google Scholar] [CrossRef]
- Li, J.; Zhao, P.; Hu, Q.; Ai, M. Robust point cloud registration based on topological graph and Cauchy weighted lq -norm. ISPRS J. Photogramm. Remote Sens. 2020, 160, 244–259. [Google Scholar] [CrossRef]
- Wu, L.S.; Wang, G.L.; Hu, Y. Iterative closest point registration for fast point feature histogram features of a volume density optimization algorithm. Meas. Control 2020, 53, 29–39. [Google Scholar] [CrossRef]
- Ramaswamy, S.; Yan, Y. Interactive modeling and simulation of virtual manufacturing assemblies: An agent-based approach. J. Intell. Manuf. 1999, 10, 503–518. [Google Scholar] [CrossRef]
- Tching, L.; Dumont, G.; Perret, J. Interactive simulation of CAD models assemblies using virtual constraint guidance. Int. J. Interact. Des. Manuf. 2010, 4, 95–102. [Google Scholar] [CrossRef]
- Liang, M.Y.; Song, Y.; Wang, Y.; Peng, X.D.; Nie, D.H. Assembly modeling technology for satellite virtual assembly. In Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD), Wellington, New Zealand, 26 April 2017; pp. 562–566. [Google Scholar]
- Jiang, S.Q.; Liu, P.; Gao, D.W.; Xu, Y.; Meng, X.; Liu, Z.Y.; Huang, Z.; Xu, R.L. Research on low cost virtual assembly training platform based on somatosensory technology. In Proceedings of the IEEE International Conference on Industrial Engineering & Engineering Management (IEEM), Singapore, 10 December 2017; pp. 250–254. [Google Scholar]
- Chen, Z.; Li, L. A new pose estimation method for non-cooperative spacecraft based on point cloud. Int. J. Intell. Comput. Cybern. 2019, 12. [Google Scholar]
- Wen, L.; He, L.; Gao, Z. Research on 3D Point Cloud De-distortion Algorithm and Its Application on Euclidean Clustering. IEEE Access 2019, 7, 86041–86053. [Google Scholar] [CrossRef]
- Li, T.F. Research on Complex Free-form Surface Parts Quality Inspection with 3D Registration Method. Ph.D. Thesis, Huazhong University of Science and Technology, Wuhan, China, 2017. [Google Scholar]
- Bao, J.S.; Li, Z.Q.; Xiang, Q.; Wu, D.L. The Modeling, Evolutionary and Application of Quasi-physical virtual assembly. J. Mech. Eng. 2018, 54, 61–69. [Google Scholar] [CrossRef]
- Zhu, X.H.; Liu, Y.M.; Liu, J.L. Application of reverse engineering to assembly and correction based on non-contact measurement. Min. Process. Eq. 2019, 46, 46–49. [Google Scholar]
- Yu, A.X. Object Scanning Mode Based Composite Product Repair Scheme Design. Master’s Thesis, Zhejiang University, Hangzhou, China, 2019. [Google Scholar]
- Zhao, Y.; Zhang, L.; Xue, Q.; Zhang, Y.T. Application of virtual technology in vehicle dimension matching. Auto Eng. 2015, 48–51. [Google Scholar] [CrossRef]
- Sun, Z.L. Research on Filtering Method of Three-Dimensional Laser Scanning Point Cloud Data. Master’s Thesis, Central South University, Changsha, China, 2011. [Google Scholar]
- Ehrlich, C. Terminological aspects of the Guide to the Expression of Uncertainty in Measurement (GUM). Metrologia 2014, 51, S145. [Google Scholar] [CrossRef]
- He, D.J.; Shao, X.N.; Wang, D.; Hu, S.J. Denoising Method of 3-D Point Cloud Data of Plants Obtained by Kinect. Trans. Chin. Soc. Agr. Machi. 2016, 47, 331–336. [Google Scholar] [CrossRef]
- Bentley, J.L. Multidimensional binary search trees used for associative searching. Commun. ACM 1975, 18, 509–517. [Google Scholar] [CrossRef]
- Altman, N.S. An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 1992, 46, 175–185. [Google Scholar]
- Rusu, R.B.; Marton, Z.C.; Blodow, N.; Dolha, M.; Beetz, M. Towards 3D point cloud based object maps for household environments. Robot. Auton. Syst. 2008, 56, 927–941. [Google Scholar] [CrossRef]
- Hoppe, H.; DeRose, T.; Duchamp, T.; McDonald, J.; Stuetzle, W. Surface reconstruction from unorganized points. In Proceedings of the 19th Annual Conference and Exhibition on Computer Graphics and Interactive Techniques (CGIT), New York, NY, USA, 27–31 July 1992; pp. 71–78. [Google Scholar]
- Lancaster, P.; Salkauskas, K. Surfaces generated by moving least squares methods. Math. Comput. 1981, 37, 141–158. [Google Scholar] [CrossRef]
- Hartigan, J.A.; Wong, M.A. A K-Means Clustering Algorithm. J. R. Stat. Soc. Ser. C 1979, 28, 100–108. [Google Scholar]
- Bezdek, J.; Ehrlich, R.; Full, W. FCM: The fuzzy c-means clustering algorithm. Comput. Geosci. 1984, 10, 191–203. [Google Scholar] [CrossRef]
- Gill, B.; Sariel, H. Efficiently approximating the minimum-volume bounding box of a point set in three dimensions. J. Algorithms 2001, 38, 91–109. [Google Scholar]
- Pauly, M.; Gross, M.; Kobbelt, L.P. Efficient simplification of point-sampled surface. In Proceedings of the IEEE Visualization (VIS 2002), Boston, MA, USA, 27 October–1 November 2002. [Google Scholar]
- Du, X.; Zhuo, Y. A Point Cloud Data Reduction Method Based on Curvature. In Proceedings of the IEEE 10th International Conference on Computer-Aided Industrial Design & Conceptual Design, Wenzhou, China, 26–29 November 2009. [Google Scholar]
- Gurram, P.; Hu, S.; Chan, A. Uniform grid upsampling of 3D lidar point cloud data. In Proceedings of the Three-Dimensional Image Processing (3DIP) and Applications 2013, Burlingame, CA, USA, 12 March 2013. [Google Scholar]
- Schnabel, R.; Klein, R. Octree-based Point-Cloud Compression. SPBG 2006, 6, 111–120. [Google Scholar]
- Wu, J.J. Research of Point-based Techniques on Unorganized Point Cloud. Ph.D. Thesis, Huazhong University of Science and Technology, Wuhan, China, 2004. [Google Scholar]
- Besl, P.J.; McKay, H.D. A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 1992, 14, 239–256. [Google Scholar] [CrossRef]
- Rusu, R.B.; Blodow, N.; Beetz, M. Fast point feature histograms (FPFH) for 3D registration. 2009 IEEE International Conference on Robotics and Automation. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), Kobe, Japan, 17 June 2009; pp. 3212–3217. [Google Scholar]
- Rao, R.V.; Savsani, V.J.; Vakharia, D.P. Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Comput. Aided. Des. 2011, 43, 303–315. [Google Scholar] [CrossRef]
- Geem, Z.W.; Kim, J.H.; Loganathan, G.V. A new heuristic optimization algorithm: Harmony Search. Simulation 2001, 2, 60–68. [Google Scholar] [CrossRef]
- Mirjalili, S. Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective discrete, and multi-objective problems. Neural. Comput. Appl. 2016, 27, 1053–1073. [Google Scholar] [CrossRef]
Measurement Parameters | Value | |
---|---|---|
| Fineness of Scanning | 7640 (point/line) |
Scan Rate | 458,400 (point/s) | |
Measurement Accuracy | 0.0240 (mm) | |
Frequency | 60 (Hz) | |
Resolution Ratio | 0.01370 (mm) | |
Temperature | 10–40 (°C) |
Part | F | |||||||
---|---|---|---|---|---|---|---|---|
Fine registration | ICP | 0.135 | −1.393 | 6.823 | −4.142 | −1.486 | −2.198 | 0.331 |
HS | 0.133 | −1.341 | 6.790 | −4.139 | −1.450 | −2.137 | 0.1132 | |
DA | 0.132 | −1.347 | 6.797 | −4.140 | −1.433 | −2.139 | 0.0740 | |
TLBO | 0.131 | −1.332 | 6.792 | −4.148 | −1.455 | −2.139 | 0.0132 |
Part | |||||||
---|---|---|---|---|---|---|---|
Coarse registration | Figure 11 | 9.0387 | 3.793 | 90.823 | 1196.752 | −1590.586 | 460.298 |
Figure 12 | −65.745 | −20.340 | −94.093 | 1166.474 | 426.493 | 314.908 | |
Figure 13 | 177.283 | 2.508 | 173.269 | 2132.986 | −728.748 | 291.238 | |
Figure 14 | 138.172 | −77.432 | 143.629 | 1299.793 | −697.308 | −732.713 | |
Figure 15 | 146.283 | 5.299 | 100.222 | 2432.986 | −511.439 | 543.314 | |
Figure 16 | 155.333 | −54.520 | 166.877 | 1115.545 | −555.358 | −753.234 | |
Figure 17 | −88.344 | −9.453 | −13.342 | 2563.111 | −657.546 | 300.455 | |
Fine registration | Figure 11 | 0.500 | 0.079 | 0.685 | −6.414 | −9.381 | 6.173 |
Figure 12 | 8.161 | 0.056 | −0.165 | 1.157 | 7.854 | 7.963 | |
Figure 13 | 0.132 | −1.342 | 6.782 | −4.144 | −1.451 | −2.137 | |
Figure 14 | 1.397 | −0.537 | 2.823 | −2.060 | −5.086 | 1.711 | |
Figure 15 | 3.445 | 0.043 | −0.133 | 0.160 | 3.444 | 2.323 | |
Figure 16 | 0.111 | −0.554 | 2.820 | −1.155 | −0.775 | −1.444 | |
Figure 17 | 1.337 | −0.666 | 2.823 | −1.041 | −2.168 | 0.911 |
Part | Min | Max | Mean | Std |
---|---|---|---|---|
Figure 18a | 0.0105 | 3.1606 | 0.6576 | 0.3838 |
Figure 18b | 0.0171 | 1.1075 | 0.3469 | 0.1761 |
Figure 18c | 0.0068 | 3.1068 | 0.8512 | 0.7559 |
Figure 18d | 0.0182 | 2.2808 | 0.3487 | 0.1715 |
Figure 18e | 0.0496 | 0.2346 | 0.2243 | 0.0070 |
Figure 18f | 0.0109 | 0.0125 | 0.1146 | 0.0046 |
Figure 18g | 0.0171 | 1.8814 | 0.3311 | 0.1806 |
Figure 18h | 0.0156 | 7.3595 | 0.4190 | 0.3115 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yang, Y.; Li, M.; Ma, X. An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data. Appl. Sci. 2020, 10, 5379. https://doi.org/10.3390/app10155379
Yang Y, Li M, Ma X. An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data. Applied Sciences. 2020; 10(15):5379. https://doi.org/10.3390/app10155379
Chicago/Turabian StyleYang, Yang, Ming Li, and Xie Ma. 2020. "An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data" Applied Sciences 10, no. 15: 5379. https://doi.org/10.3390/app10155379
APA StyleYang, Y., Li, M., & Ma, X. (2020). An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data. Applied Sciences, 10(15), 5379. https://doi.org/10.3390/app10155379