Development of 3D environmental laser scanner using pinhole projection

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.227629

Keywords:

three-dimensional laser scanners, visualization, camera calibration, pinhole projection, 3D reconstruction

Abstract

Three-dimensional (3D) information of capturing and reconstructing an object existing in its environment is a big challenge. In this work, we discuss the 3D laser scanning techniques, which can obtain a high density of data points by an accurate and fast method. This work considers the previous developments in this area to propose a developed cost-effective system based on pinhole projection concept and commercial hardware components taking into account the current achieved accuracy. A laser line auto-scanning system was designed to perform close-range 3D reconstructions for home/office objects with high accuracy and resolution. The system changes the laser plane direction with a microcontroller to perform automatic scanning and obtain continuous laser strips for objects’ 3D reconstruction. The system parameters were calibrated with Matlab’s built-in camera calibration toolbox to find camera focal length and optical center constraints. The pinhole projection equation was defined to optimize the prototype rotating axis equation. The developed 3D environmental laser scanner with pinhole projection proved the system’s effectiveness on close-range stationary objects with high resolution and accuracy with a measurement error in the range (0.05–0.25) mm. The 3D point cloud processing of the Matlab computer vision toolbox has been employed to show the 3D object reconstruction and to perform the camera calibration, which improves efficiency and highly simplifies the calibration method. The calibration error is the main error source in the measurements, and the errors of the actual measurement are found to be influenced by several environmental parameters. The presented platform can be equipped with a system of lower power consumption, and compact smaller size

Author Biography

Lateef Abd Zaid Qudr, AlSafwa University College

Doctor of Computer Sciences, Senior Lecturer

Department of Computer Techniques Engineering

References

  1. Arayici, Y., Hamilton, A., Gamito, P., Albergaria, G. (2004). The Scope in the INTELCITIES Project for the Use of the 3D Laser Scanner. Proceedings of the Fourth International Conference on Engineering Computational Technology. doi: https://doi.org/10.4203/ccp.80.51
  2. Huber, D. F. (2002). Automatic three-dimensional modeling from reality. CMU-RI-TR-02-35. The Robotics Institute, 201. Available at: https://www.ri.cmu.edu/pub_files/pub3/huber_daniel_2002_1/huber_daniel_2002_1.pdf
  3. Bernardini, F., Rushmeier, H. E. (2000). Strategies for registering range images from unknown camera positions. Three-Dimensional Image Capture and Applications III. doi: https://doi.org/10.1117/12.380042
  4. Chibane, J., Alldieck, T., Pons-Moll, G. (2020). Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi: https://doi.org/10.1109/cvpr42600.2020.00700
  5. Biffi, C., Cerrolaza, J. J., Tarroni, G., de Marvao, A., Cook, S. A., O’Regan, D. P., Rueckert, D. (2019). 3D High-Resolution Cardiac Segmentation Reconstruction From 2D Views Using Conditional Variational Autoencoders. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). doi: https://doi.org/10.1109/isbi.2019.8759328
  6. Lague, D., Brodu, N., Leroux, J. (2013). Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z). ISPRS Journal of Photogrammetry and Remote Sensing, 82, 10–26. doi: https://doi.org/10.1016/j.isprsjprs.2013.04.009
  7. Guisado-Pintado, E., Jackson, D. W. T., Rogers, D. (2019). 3D mapping efficacy of a drone and terrestrial laser scanner over a temperate beach-dune zone. Geomorphology, 328, 157–172. doi: https://doi.org/10.1016/j.geomorph.2018.12.013
  8. Aicardi, I., Dabove, P., Lingua, A., Piras, M. (2017). Integration between TLS and UAV photogrammetry techniques for forestry applications. iForest - Biogeosciences and Forestry, 10 (1), 41–47. doi: https://doi.org/10.3832/ifor1780-009
  9. Biasion, A., Bornaz, L., Rinaudo, F. (2005). Laser Scanning Applications on Disaster Management. Geo-Information for Disaster Management, 19–33. doi: https://doi.org/10.1007/3-540-27468-5_2
  10. Zhang, J., Singh, S. (2016). Low-drift and real-time lidar odometry and mapping. Autonomous Robots, 41 (2), 401–416. doi: https://doi.org/10.1007/s10514-016-9548-2
  11. Chen, Y., Wang, J., Li, J., Lu, C., Luo, Z., Xue, H., Wang, C. (2018). LiDAR-Video Driving Dataset: Learning Driving Policies Effectively. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. doi: https://doi.org/10.1109/cvpr.2018.00615
  12. Krüsi, P., Furgale, P., Bosse, M., Siegwart, R. (2016). Driving on Point Clouds: Motion Planning, Trajectory Optimization, and Terrain Assessment in Generic Nonplanar Environments. Journal of Field Robotics, 34 (5), 940–984. doi: https://doi.org/10.1002/rob.21700
  13. Huang, X., Cheng, X., Geng, Q., Cao, B., Zhou, D., Wang, P. et. al. (2018). The ApolloScape Dataset for Autonomous Driving. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). doi: https://doi.org/10.1109/cvprw.2018.00141
  14. Hu, H., Zhao, T., Wang, Q., Gao, F., He, L. (2020). R-CNN Based 3D Object Detection for Autonomous Driving. CICTP 2020. doi: https://doi.org/10.1061/9780784483053.077
  15. Li, B., Ouyang, W., Sheng, L., Zeng, X., Wang, X. (2019). GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi: https://doi.org/10.1109/cvpr.2019.00111
  16. Wang, X., Jiang, M., Zhou, Z., Gou, J., Hui, D. (2017). 3D printing of polymer matrix composites: A review and prospective. Composites Part B: Engineering, 110, 442–458. doi: https://doi.org/10.1016/j.compositesb.2016.11.034
  17. Zaharia, C., Gabor, A.-G., Gavrilovici, A., Stan, A. T., Idorasi, L., Sinescu, C., Negruțiu, M.-L. (2017). Digital Dentistry – 3D Printing Applications. Journal of Interdisciplinary Medicine, 2 (1), 50–53. doi: https://doi.org/10.1515/jim-2017-0032
  18. Thrun, S., Burgard, W., Fox, D. (2000). A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping. Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065). doi: https://doi.org/10.1109/robot.2000.844077
  19. Skotheim, O., Lind, M., Ystgaard, P., Fjerdingen, S. A. (2012). A flexible 3D object localization system for industrial part handling. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. doi: https://doi.org/10.1109/iros.2012.6385508
  20. Lindner, L., Sergiyenko, O., Rodríguez-Quiñonez, J. C., Rivas-Lopez, M., Hernandez-Balbuena, D., Flores-Fuentes, W. et. al. (2016). Mobile robot vision system using continuous laser scanning for industrial application. Industrial Robot: An International Journal, 43 (4), 360–369. doi: https://doi.org/10.1108/ir-01-2016-0048
  21. Kriegel, S., Bodenmuller, T., Suppa, M., Hirzinger, G. (2011). A surface-based Next-Best-View approach for automated 3D model completion of unknown objects. 2011 IEEE International Conference on Robotics and Automation. doi: https://doi.org/10.1109/icra.2011.5979947
  22. Blais, F., Picard, M., Godin, G. (2004). Accurate 3D acquisition of freely moving objects. Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004. doi: https://doi.org/10.1109/tdpvt.2004.1335269
  23. Zhongdong, Y., Peng, W., Xiaohui, L., Changku, S. (2014). 3D laser scanner system using high dynamic range imaging. Optics and Lasers in Engineering, 54, 31–41. doi: https://doi.org/10.1016/j.optlaseng.2013.09.003
  24. Tocheri, M. W. (2009). Laser Scanning: 3D Analysis of Biological Surfaces. Advanced Imaging in Biology and Medicine, 85–101. doi: https://doi.org/10.1007/978-3-540-68993-5_4
  25. Hennessy, R. J., Kinsella, A., Waddington, J. L. (2002). 3D laser surface scanning and geometric morphometric analysis of craniofacial shape as an index of cerebro-craniofacial morphogenesis: initial application to sexual dimorphism. Biological Psychiatry, 51 (6), 507–514. doi: https://doi.org/10.1016/s0006-3223(01)01327-0
  26. Leone, A., Diraco, G., Distante, C. (2007). Stereoscopic System for 3-D Seabed Mosaic Reconstruction. 2007 IEEE International Conference on Image Processing. doi: https://doi.org/10.1109/icip.2007.4379212
  27. Drap, P., Seinturier, J., Scaradozzi, D., Gambogi, P., Long, L., Gauch, F. (2007). Photogrammetry for virtual exploration of underwater archeological sites. XXI International CIPA Symposium. Athens.
  28. Bianco, G., Gallo, A., Bruno, F., Muzzupappa, M. (2013). A Comparative Analysis between Active and Passive Techniques for Underwater 3D Reconstruction of Close-Range Objects. Sensors, 13 (8), 11007–11031. doi: https://doi.org/10.3390/s130811007
  29. Wang, B., Jiang, L., Li, J. W., Cai, H. G., Liu, H. (2005). Grasping unknown objects based on 3d model reconstruction. Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. doi: https://doi.org/10.1109/aim.2005.1511025
  30. Rusu, R. B., Blodow, N., Marton, Z. C., Beetz, M. (2009). Close-range scene segmentation and reconstruction of 3D point cloud maps for mobile manipulation in domestic environments. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. doi: https://doi.org/10.1109/iros.2009.5354683
  31. Rusu, R. B. (2010). Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments. KI - Künstliche Intelligenz, 24 (4), 345–348. doi: https://doi.org/10.1007/s13218-010-0059-6
  32. Beall, C., Lawrence, B. J., Ila, V., Dellaert, F. (2010). 3D reconstruction of underwater structures. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. doi: https://doi.org/10.1109/iros.2010.5649213
  33. Straub, J., Kerlin, S. (2014). Development of a Large, Low-Cost, Instant 3D Scanner. Technologies, 2 (2), 76–95. doi: https://doi.org/10.3390/technologies2020076
  34. Louvrier, A., Marty, P., Barrabé, A., Euvrard, E., Chatelain, B., Weber, E., Meyer, C. (2017). How useful is 3D printing in maxillofacial surgery? Journal of Stomatology, Oral and Maxillofacial Surgery, 118 (4), 206–212. doi: https://doi.org/10.1016/j.jormas.2017.07.002
  35. Birtchnell, T., Hoyle, W., Birtchnell, T., Hoyle, W. (2016). What is 3D Printing? The definitive guide. 3D Print Dev Glob South.
  36. Arayici, Y. (2007). An approach for real world data modelling with the 3D terrestrial laser scanner for built environment. Automation in Construction, 16 (6), 816–829. doi: https://doi.org/10.1016/j.autcon.2007.02.008
  37. Chi, S., Xie, Z., Chen, W. (2016). A Laser Line Auto-Scanning System for Underwater 3D Reconstruction. Sensors, 16 (9), 1534. doi: https://doi.org/10.3390/s16091534
  38. Allegra, D., Gallo, G., Inzerillo, L., Lombardo, M., Milotta, F. L. M., Santagati, C. et. al. (2016). Low cost handheld 3D scanning for architectural elements acquisition. STAG: Smart Tools and Apps in computer Graphics. doi: https://dx.doi.org/10.2312/stag.20161372
  39. Reshetyuk, Y. (2006). Calibration of terrestrial laser scanners Callidus 1.1, Leica HDS 3000 and Leica HDS 2500. Survey Review, 38 (302), 703–713. doi: https://doi.org/10.1179/sre.2006.38.302.703
  40. Bauwens, S., Bartholomeus, H., Calders, K., Lejeune, P. (2016). Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning. Forests, 7 (12), 127. doi: https://doi.org/10.3390/f7060127
  41. Lee, S. Y., Majid, Z., Setan, H. (2013). 3D data acquisition for indoor assets using terrestrial laser scanning. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II-2/W1, 221–226. doi: https://doi.org/10.5194/isprsannals-ii-2-w1-221-2013
  42. Abbas, M. A., Setan, H., Majid, Z., Chong, A. K., Idris, K. M., Aspuri, A. (2013). Calibration and Accuracy Assessment of Leica ScanStation C10 Terrestrial Laser Scanner. Developments in Multidimensional Spatial Data Models, 33–47. doi: https://doi.org/10.1007/978-3-642-36379-5_3
  43. Wolk, R. M. (2008). Utilizing Google Earth and Google Sketchup to visualize wind farms. 2008 IEEE International Symposium on Technology and Society. doi: https://doi.org/10.1109/istas.2008.4559793
  44. LOGITECH ® HD PRO WEBCAM C920. Available at: https://docs.rs-online.com/97f0/A700000006917072.pdf
  45. MeshLab. Available at: https://www.meshlab.net/

Downloads

Published

2021-04-20

How to Cite

Qudr, L. A. Z. (2021). Development of 3D environmental laser scanner using pinhole projection . Eastern-European Journal of Enterprise Technologies, 2(1 (110), 37–43. https://doi.org/10.15587/1729-4061.2021.227629

Issue

Vol. 2 No. 1 (110) (2021): Engineering technological systems

Section

Engineering technological systems

License

Copyright (c) 2021 Lateef Abd Zaid Qudr

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.