Implementation of a program for real-time processing and visualization of LIDAR scan data in the LabVIEW programming environment

Implementation of a program for real-time processing and visualization of LIDAR scan data in the LabVIEW programming environment

Valeriy L. Alekseev
Russian State Scientific Center for Robotics and Technical Cybernetics (RTC), Leading Engineer, 21, Tikhoretsky pr., Saint Petersburg, 194064, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.

Dmitry A. Goryachkin
RTC, Senior Research Scientist, 21, Tikhoretsky pr., Saint Petersburg, 194064, Russia, tel.: +7(921)924-29-38, This email address is being protected from spambots. You need JavaScript enabled to view it.

Viktor I. Kuprenyuk
PhD in Physics and Mathematics, RTC, Leading Research Scientist, 21, Tikhoretsky pr., Saint Petersburg, 194064, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.

Evgeniy N. Sosnov
RTC, Senior Research Scientist, 21, Tikhoretsky pr., Saint Petersburg, 194064, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.

Nikolay L. Schegrov
RTC, Programmer, 21, Tikhoretsky pr., Saint Petersburg, 194064, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.


Received December 21, 2021

Abstract
The features of development and optimization of software for real-time LIDAR data processing are considered. The advantages of the LabVIEW graphical development environment for creating highly optimized applications using parallel execution threads and pipelined data processing are shown.

Key words
laser scanning, LIDAR, parallel computing, multithreading, real-time data processing, LabVIEW, visualization, point cloud.

Acknowledgements
The work was carried out with the financial support of the Ministry of Education and Science of the Russian Federa-tion within the scope of state assignment No. 075-00913-21-03.

DOI
10.31776/RTCJ.10303

Bibliographic description
Alekseev, V.L. et al., 2022. Implementation of a program for real-time processing and visualization of LIDAR scan data in the LabVIEW programming environment. Robotics and Technical Cybernetics, 10(3), pp.179-189.

UDC identifier:
004.4'236:681.786.23

References 

  1. Sojan Lal, P., Unnikrishnan, A. and Poulose Jacob, K., 1998. Parallel implementation of octtree generation algorithm. In: Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269). IEEE. DOI: 10.1109/ICIP.1998.727419.
  2. Kozlov, D. and Turlapov, V., 2010. Algoritm vosstanovleniya poverkhnosti iz oblaka tochek na graficheskom protsessore [Algorithm for restoring a surface from a point cloud on a GPU]. In: 20-ya Mezhdunarodnaya Konferentsiya po Komp'yuternoy Grafike i Zreniyu «GraphiCon 2010»: Trudy Konferentsii [20th International Conference on Computer Graphics and Vision «GraphiCon 2010»: Proceedings of the Conference]. Available at: https://www.graphicon.ru/html/2010/conference/RU/Se5/44.pdf (Accessed 8 June 2022). (in Russian).
  3. Pointclouds, u.d. Point cloud library. Documentation. Available at: https://pointclouds.org/documentation/ (Accessed 8 June 2022).
  4. NI, u.d. Programming strategies for multicore processing: Task parallelism. Available at: <https://www.ni.com/ru-ru/support/documentation/supplemental/07/programming-strategies-for-multicore-processing--task-parallelis.html> (Accessed 8 June 2022).
  5. NI, u.d. LabVIEW GPU analysis toolkit. Available at: <https://zone.ni.com/reference/en-XX/help/373575A-01/lvgpu/lvgpu> (Accessed 8 June 2022).
  6. Elseberg, Jan et al., 2012. Comparison of nearest-neighbor-search strategies and implementations for efficient shape registration. Journal of Software Engineering for Robotics 3.1 (2012): 2-12. Available at: <https://www.researchgate.net/publication/233792571_Comparison_on_nearest-neigbour-search_strategies_and_implementations_for_efficient_shape_registration> (Accessed 9 June 2022).
  7. Mogilko, A.A., 2013. Parallel'nyy algoritm poiska blizhayshey tochki v radiuse [Parallel algorithm for finding the nearest point in a radius]. Nauka i Obrazovanie: Nauchnoe Izdanie MGTU im. N.E. Baumana [Science and Education of Bauman MSTU], 11, pp.363-382. (in Russian).
  8. Yianilos, P.N., 1993. Data structures and algorithms for nearest neighbor search in general metric spaces. In: SODA '93: Proceedings of the Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, pp.311–321. Available at: <https://dl.acm.org/doi/10.5555/313559.313789> (Accessed 9 June 2022).
  9. Panigrahy, R., 2008. An improved algorithm finding nearest neighbor using Kd-trees. Microsoft Research, Mountain View CA. Available at: <http://theory.stanford.edu/~rinap/papers/kdtreelatin.pdf> (Accessed 9 June 2022).
  10. Point Cloud Library, u.d. Spatial Partitioning and Search Operations with Octrees. Available at: <https://pcl.readthedocs.io/en/latest/octree.html#octree-search> (Accessed 9 June 2022).
  11. Pan, J and Manocha D., 2011. Fast GPU-based Locality Sensitive Hashing for K-Nearest Neighbor Computation. In: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS). Available at: <URL: http://gamma.cs.unc.edu/KNN/gpuknn.pdf> (Accessed 9 June 2022).
  12. Belyaevskiy, K.O., 2019. Formirovanie oktodereva po oblaku tochek pri ogranichenii ob"ema operativnoy pamyati [Formation of an octree by a point cloud with limited RAM]. Nauchno-Tekhnicheskie Vedomosti SPbGPU. Seriya: Informatika, Telekommunikatsii i Upravlenie [Scientific and Technical Statements of SPbSPU. Series: Informatics, Telecommunications and Management], 12-4, pp.97-110. D0I: 10.18721/JCSTCS.12408 (in Russian).
  13. Dyn, N., Iske, A. and Wendland H., 2008. Meshfree thinning of 3D POINT CLOUDS. Found Comput Math, 8, pp.409-425. DOI: 10.1007/s10208-007-9008-7.
  14. NI, u.d. Programming strategies for multicore processing: pipelining. Available at: <https://www.ni.com/ru-ru/support/documentation/supplemental/07/programming-strategies-for-multicore-processing--pipelining.html> (Accessed 9 June 2022).
  15. NI, u.d. Configuring parallel for loop iterations to tweak performance. Available at: <https://www.ni.com/docs/ru-RU/bundle/labview-2020/page/lvhowto/configuring_parallel_for_loop_iterations.html (Accessed 9 June 2022).
  16. Медведев, В.И. and Райкова, Л.С., 2017. Programmy dlya obrabotki dannykh lazernogo skanirovaniya mestnosti [Programs for processing data of laser scanning of the area]. SAPR i GIS avtomobil'nykh dorog [CAD & GIS for roads], 2(9). Available at: <URL: http://www.cadgis.ru/2017/9/CADGIS-2017-2(9)-02.Medvedev-Raikova(LIDAR-processing-apps).pdf> (Accessed 9 June 2022). (in Russian).
  17. FARO u.d. SCENE software. Available at: <https://www.faro.com/en/Resource-Library/Tech-Sheet/techsheet-faro-scene> (Accessed 9 June 2022).
  18. RIEGL u.d. Software packages. RiACQUIRE. Available at: <http://www.riegl.com/products/software-packages/riacquire/> (Accessed 9 June 2022).
  19. Leica-geosystems u.d. Leica Cyclone 3D Point Cloud Processing Software. Available at: <https://leica-geosystems.com/products/laser-scanners/software/leica-cyclone> (Accessed 9 June 2022).
  20. Dzhdid, A.D., 2019. Obzor metodov segmentatsii i klassifikatsii oblaka tochek arkhitekturnykh ob"ektov [Overview of methods for segmentation and classification of a point cloud of architectural objects]. Izvestiya Vysshikh Uchebnykh Zavedeniy. Seriya: Geodeziya i Aerofotos"emka [News of Higher Educational Institutions. Series: Geodesy and Aerial Photography], 63-1, p.52.
  21. Lindner, P. anl Wanielik, G., 2009. 3D LIDAR processing for vehicle safety and environment recognition. 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, pp.66-71. DOI: 10.1109/CIVVS.2009.4938725.
  22. Butenko, M.Yu., 2019. Metod approksimatsii oblaka tochek STZ dlya statsionarnoy 2D stseny [VSW Point Cloud Approximation Method for a Stationary 2D Scene]. Izvestiya Yuzhnogo Federal'nogo Universiteta. Seriya: Tekhnicheskie Nauki [Izvestiya SFedU. Engineering Sciences], 1(203). Available at: https://cyberleninka.ru/article/n/metod-approksimatsii-oblaka-tochek-stz-dlya-statsionarnoy-2d-stseny (Accessed 9 June 2022).