Skip to main content
SpringerLink
Log in

Research on high-resolution improved projection 3D localization algorithm and precision assembly of parts based on virtual reality

  • S.I. : Machine Learning Applications for Self-Organized Wireless Networks
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Traditional assembly process design tasks are generally performed manually by experienced craftsmen using 2D drawings, which often require the use of physical prototypes. This kind of assembly process design mode inevitably has the defects of low optimization degree, low design efficiency, and high cost. The current computer-aided assembly process design also has problems such as “combination explosion.” Assembly process design technology and algorithm based on virtual reality and artificial intelligence is an effective way to solve the above problems. The application of the system in a virtual reality system provides an accurate positioning method in a virtual reality system. The ultrasonic three-dimensional space positioning system uses a differential method to improve the ranging accuracy. The system has the advantages of strong anti-electromagnetic interference, insensitivity to light and no electromagnetic radiation; thus, it is suitable for application in virtual reality systems. In the paper, two different methods of assembly process planning are proposed for interactive constraint definition assembly and automatic constraint recognition assembly, which make up for the lack of a single method, make the assembly process more realistic, and realize the assembly path by acquiring sampling points. The recording and playback of the assembly process planning process is achieved using screenshots and video compression techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Hesch JA, Kottas DG, Bowman SL, Roumeliotis SI (2014) Camera-IMU-based localization: observability analysis and consistency improvement. Int J Robot Res 33(1):182–201

    Article  Google Scholar 

  2. Zuo C, Huang L, Zhang M, Chen Q, Asundi A (2016) Temporal phase unwrapping algorithms for fringe projection profilometry: a comparative review. Opt Lasers Eng 85:84–103

    Article  Google Scholar 

  3. Li H, Ding H, Huang D, Wang Y, Zhao X, Morvan JM, Chen L (2015) An efficient multimodal 2D + 3D feature-based approach to automatic facial expression recognition. Comput Vis Image Underst 140:83–92

    Article  Google Scholar 

  4. Han G, Zhang C, Liu T, Shu L (2016) MANCL: a multi-anchor nodes collaborative localization algorithm for underwater acoustic sensor networks. Wirel Commun Mob Comput 16(6):682–702

    Article  Google Scholar 

  5. Huang J, Sun M, Gumpper K, Chi Y, Ma J (2015) 3D multifocus astigmatism and compressed sensing (3D MACS) based superresolution reconstruction. Biomed Opt Express 6(3):902–917

    Article  Google Scholar 

  6. Kumar A, Khosla A, Saini JS, Sidhu SS (2015) Range-free 3D node localization in anisotropic wireless sensor networks. Appl Soft Comput 34:438–448

    Article  Google Scholar 

  7. Svärm L, Enqvist O, Kahl F, Oskarsson M (2017) City-scale localization for cameras with known vertical direction. IEEE Trans Pattern Anal Mach Intell 39(7):1455–1461

    Article  Google Scholar 

  8. Barnowski R, Haefner A, Mihailescu L, Vetter K (2015) Scene data fusion: real-time standoff volumetric gamma-ray imaging. Nucl Instrum Methods Phys Res Sect A 800:65–69

    Article  Google Scholar 

  9. Dogancay K (2015) 3D pseudolinear target motion analysis from angle measurements. IEEE Trans Signal Process 63(6):1570–1580

    Article  MathSciNet  MATH  Google Scholar 

  10. Wu S (2015) A traffic motion object extraction algorithm. Int J Bifurc Chaos 25(14):810

    Article  Google Scholar 

  11. Khorshed RA, Hawkins ED, Duarte D, Scott MK, Akinduro OA, Rashidi NM, Spitaler M, Celso CL (2015) Automated identification and localization of hematopoietic stem cells in 3D intravital microscopy data. Stem Cell Rep 5(1):139–153

    Article  Google Scholar 

  12. Turner E, Cheng P, Zakhor A (2015) Fast, automated, scalable generation of textured 3D models of indoor environments. IEEE J Sel Top Signal Process 9(3):409–421

    Article  Google Scholar 

  13. Park HS, Shiratori T, Matthews I, Sheikh Y (2015) 3D trajectory reconstruction under perspective projection. Int J Comput Vis 115(2):115–135

    Article  MathSciNet  MATH  Google Scholar 

  14. Kanaris L, Kokkinis A, Fortino G, Liotta A, Stavrou S (2016) Sample size determination algorithm for fingerprint-based indoor localization systems. Comput Netw 101:169–177

    Article  Google Scholar 

  15. Li X, Deng ZD, Rauchenstein LT, Carlson TJ (2016) Contributed review: source-localization algorithms and applications using time of arrival and time difference of arrival measurements. Rev Sci Instrum 87(4):041502

    Article  Google Scholar 

  16. Charmette B, Royer E, Chausse F (2016) Vision-based robot localization based on the efficient matching of planar features. Mach Vis Appl 27(4):415–436

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the Research Center of Industrial Design, Research Base of Humanities and Social Sciences, Sichuan Provincial Department of Education (Grant: GY-14YB-06), the Modern Design and Culture Research Center, Research Base of Sichuan Philosophy and Social Science(Grant: MD17Z001), the Research Center of Industrial Design, Research Base of Humanities and Social Sciences, Sichuan Provincial Department of Education (Grant: GY-16ZD-01), the Research Center of Industrial Design, Research Base of Humanities and Social Sciences, Sichuan Provincial Department of Education (Grant: GY-13YB-09).

Author information

Authors and Affiliations

  1. School of Manufacturing Science and Engineering, Sichuan University, Chengdu, China

    Xun Zhang, Guofu Yin & Na Qi

  2. School of Art and Design, Xihua University, Chengdu, China

    Na Qi

Authors
  1. Xun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guofu Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Na Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Na Qi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Yin, G. & Qi, N. Research on high-resolution improved projection 3D localization algorithm and precision assembly of parts based on virtual reality. Neural Comput & Applic 31 (Suppl 1), 103–111 (2019). https://doi.org/10.1007/s00521-018-3665-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3665-0

Keywords

Search

Navigation