Object-oriented reconstruction of manipulator’s working area by point cloud

Object-oriented reconstruction of manipulator’s working area by point cloud

Anton M. Korsakov
Russian State Scientific Center for Robotics and Technical Cybernetics (RTC), Mathematician, 21, Tikhoretsky pr., Saint-Petersburg, 194064, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.

Lubov' A. Astapova
RTC, Mathematician, 21, Tikhoretsky pr., Saint-Petersburg, 194064, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.

Ekaterina Yu. Smirnova
RTC, Deputy Head of Research and Development Center (R&D Center), 21, Tikhoretsky pr., Saint-Petersburg, 194064, Russia, tel.: +7(812)552-47-10, This email address is being protected from spambots. You need JavaScript enabled to view it.


Received 03 August 2020

Abstract
Operations involving direct contact of the robot's gripping device with surrounding objects are among the most difficult in the field of robotics engineering. Therefore, minimizing the risk of tool damage and processing disruption is important. This can be achieved by using the information about the geometric parameters of the object in question and its relative to the grip position. This paper discusses approaches in reconstructing the surface of an object located in the working area of the manipulator using a point cloud, and also offers a method for describing the object by rotation surfaces using a noisy and incomplete point cloud obtained using a stereoscopic system installed on the final link of the manipulator.

Key words
Computer vision; manipulator; reconstruction of the work area, object-oriented segmentation.

DOI
https://doi.org/10.31776/RTCJ.8305

Bibliographic description
Korsakov, A., Astapova, L. and Smirnova, E., 2020. Object-oriented reconstruction of manipulator’s working area by point cloud. Robotics and Technical Cybernetics, 8(3), pp.198-205.

UDC identifier:
629.053

References

  1. Gromoshinskii, D.A. et al., 2018. Providing safe ground sampling inside the working zone of a manipulator with computer vision. In: Proceedings of the International Scientific and Technological Conference on Extreme Robotics and Conversion Tendencies. Saint-Petersburg, pp.185-194. (In Russian).
  2. Smirnova, E.Y., Stepanov, D.N. and Goryunov, V.V., 2016. A technique of natural visual landmarks detection and description for mobile robot cognitive navigation. In: Proceedings of the 26th International DAAAM Symposium 2016, DAAAM. Pp.0905-0911.
  3. Alliez, P. et al., 2007. Voronoi-based variational reconstruction of unoriented point sets. In: ACM Trans. Graph. Pp. 39-48.
  4. Kazhdan, M. and Hoppe, H., 2013. Screened Poisson surface reconstruction. ACM Trans. Graph., 32(3).
  5. De Loera, J.A., Rambau, J. and Santos, F., 2010. Triangulations, structures for algorithms and applications. Algorithms and Computation in Mathematics, 25(2).
  6. Bernardini, F. et al., 1999. The ball-pivoting algorithm for surface reconstruction. IEEE Transactions on Visualization And Computer Graphics, 5(4).
  7. Behshad, A. and Ghasemil, M.R., 2014. Nurbsiga-based modelling: Analysis and optimization of laminated plates. Tehnichki vjesnik, 21(4), pp.789-797.
  8. Cai, J. et al., 2016. Skeletal Animation with Anisotropic Materials, Graphical Simulation of Deformable Models. Springer International Publ., pp.85-104.
  9. Yu, L., Yu, J. and Wang, Z., 2017. A realistic 3D articulatory animation system for emotional visual pronunciation. Multimed. Tools Appl., 76(18), pp.19241-19262.
  10. Xiong, H. et al., 2015. A method for accurate reconstructions of the upper airway using magnetic resonance images. PLoS One, 10(6), pp.1-14.
  11. Galassi, F. et al., 2018. 3D reconstruction of coronary arteries from 2D angiographic projections using nonuniform rational basis splines (NURBS) for accurate modelling of coronary stenoses. PLoS One, 13(1), pp.1-23.
  12. Midorikawa, Y. and Masuda, H., 2018. Extraction of surfaces using section curves for engineering plants. In: Proceedings of the ASME Design Engineering Technical Conference, 1B.
  13. Schnabel, R., Wahl, R. and Klein, R., 2007. Efficient RANSAC for Point-Cloud Shape Detection. Computer Graphics Forum, 26, pp.214-226.
  14. Kawashima, K., Kanai, S. and Date, H., 2014. As-built modelling of piping system from terrestrial laser-scanned point clouds using normal-based region growing. J. Comput. Des. Eng. Elsevier Masson SAS, 1(1), pp.13-26.
  15. Bojarovski, S., 2014. Robust Tracking-Based Skeleton Reconstruction of Cold-Water Corals from Computer Tomography Images. Saint-Petersburg: Saint-Petersburg State University Publ. (In Russian).
  16. Yureidini, A., Kerrien, E. and Cotin, S., 2012. Robust RANSAC-based blood vessel segmentation. SPIE Medical Imaging.
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