The article describes a method of simulated 3D scanning of triangle meshes based on ray casting which is used to find the optimal configuration of a real 3D scanner turntable. The configuration include the number of scanners, their elevation above the rotary table and the number of required rotation steps. The evaluation is based on the percentage of the part surface covered by the resulting point cloud, which determines the ability to capture all details of the shape. Principal component analysis is used as a secondary criterion to also evaluate the ability to capture the overall general proportions of the model.

Department of Robotics Faculty of Mechanical Engineering VSB Technical University of Ostrava 17 Listopadu 2172 15 708 00 Ostrava Poruba Czech Republic

Faculty of Mechanical Engineering Technical University of Košice 04200 Košice Slovakia

Zobrazit více v PubMed

Kot T., Novak P., Babjak J., Olivka P. Modelling and Simulation for Autonomous Systems, MESAS 2016. Volume 9991. Springer International Publishing; Cham, Switzerland: 2016. Rendering of 3D Maps with Additional Information for Operator of a Coal Mine Mobile Robot; pp. 214–225. Lecture Notes in Computer Science.

Ulas C., Temeltas H. A Fast and Robust Feature-Based Scan-Matching Method in 3D SLAM and the Effect of Sampling Strategies. Int. J. Adv. Robot. Syst. 2013;10:396. doi: 10.5772/56964. DOI

Son S., Park H., Lee K.H. Automated laser scanning system for reverse engineering and inspection. Int. J. Mach. Tools Manuf. 2002;42:889–897. doi: 10.1016/S0890-6955(02)00030-5. DOI

Yao A. Applications of 3D scanning and reverse engineering techniques for quality control of quick response products. Int. J. Adv. Manuf. Technol. 2005;26:1284–1288. doi: 10.1007/s00170-004-2116-5. DOI

Rosicky J., Grygar A., Chapcak P., Bouma T., Rosicky J. Application of 3D Scanning in Prosthetic and Orthotic Clinical Practice; Proceedings of the 7th International Conference on 3D Body Scanning Technologies; Lugano, Switzerland. 30 November–1 December 2016; pp. 88–97. DOI

Wachowiak M.J., Karas B.V. 3d Scanning and Replication for Museum and Cultural Heritage Applications. J. Am. Inst. Conserv. 2009;48:141–158. doi: 10.1179/019713609804516992. DOI

Hruboš M., Nemec D., Janota A., Pirník R., Bubeníková E., Gregor M., Juhásová B., Juhás M. Sensor fusion for creating a three-dimensional model for mobile robot navigation. Int. J. Adv. Robot. Syst. 2019;16:1729881419865072. doi: 10.1177/1729881419865072. DOI

Ghani M.F.A., Sahari K.S.M. Detecting negative obstacle using Kinect sensor. Int. J. Adv. Robot. Syst. 2017;14 doi: 10.1177/1729881417710972. DOI

Thalmann R., Meli F., Küng A. State of the Art of Tactile Micro Coordinate Metrology. Appl. Sci. 2016;6:150. doi: 10.3390/app6050150. DOI

Maru M.B., Lee D., Tola K.D., Park S. Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections. Sensors. 2021;21:201. doi: 10.3390/s21010201. PubMed DOI PMC

Ma Z., Sun D., Xu H., Zhu Y., He Y., Cen H. Optimization of 3D Point Clouds of Oilseed Rape Plants Based on Time-of-Flight Cameras. Sensors. 2021;21:664. doi: 10.3390/s21020664. PubMed DOI PMC

Laurenzis M., Bacher E. 3D Imaging from Coded Range-Gated Images; Proceedings of the 3rd Workshop on Active Imaging, Saint-Louis; France. 22 January 2011.

Hashizume H., Sugimito M. 1P1-K04 A Fast And Accurate Distance Measurement Method Using RF Modulated Light(Future of Optronics) [(accessed on 5 July 2021)]; Available online: https://www.jstage.jst.go.jp/article/jsmermd/2012/0/2012__1P1-K04_1/_pdf/-char/ja.

Babar M., Khan I.M., Misal U., Mudassir M. A Fundamental Analysis of Conventional Laser Triangulation Technique for the Development of Revamped 3D Scanning System; Proceedings of the 2020 International Conference on Engineering and Emerging Technologies (ICEET); Lahore, Pakistan. 22–23 February 2020; pp. 1–6. DOI

Zhang S. High-speed 3D shape measurement with structured light methods: A review. Opt. Lasers Eng. 2018;106:119–131. doi: 10.1016/j.optlaseng.2018.02.017. DOI

Keselman L., Iselin Woodfill J., Grunnet-Jepsen A., Bhowmik A. Intel RealSense Stereoscopic Depth Cameras; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops; Honolulu, HI, USA. 15 May 2017.

Woodham R. Photometric Method for Determining Surface Orientation from Multiple Images. Opt. Eng. 1992;19 doi: 10.1117/12.7972479. DOI

Baqersad J., Poozesh P., Niezrecki C., Avitabile P. Photogrammetry and optical methods in structural dynamics—A review. Mech. Syst. Signal Process. 2017;86:17–34. doi: 10.1016/j.ymssp.2016.02.011. DOI

Elbaz G., Avraham T., Fischer A. 3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Honolulu, HI, USA. 15 May 2017.

Danhof M., Schneider T., Laube P., Umlauf G. A Virtual-Reality 3d-Laser-Scan Simulation; Proceedings of the BW-CAR SINCOM Conference; Konstanz, Germany. 10 January 2015.

Al-Temeemy A.A. The Development of a 3D LADAR Simulator Based on a Fast Target Impulse Response Generation Approach. 3D Res. 2017;8:31. doi: 10.1007/s13319-017-0142-y. DOI

Budge S., Leishman B., Pack R. Laser Radar Technology and Applications XI. Volume 6214. SPIE; Bellingham, WA, USA: 2006. Simulation and modeling of return waveforms from a ladar beam footprint in USU LadarSIM; pp. 204–213.

Coyac A., Hespel L., Riviere N., Briottet X. Electro-Optical and Infrared Systems: Technology and Applications XII; and Quantum Information Science and Technology. Volume 9648. SPIE; Bellingham, USA: 2015. Performance assessment of simulated 3D laser images using Geiger-mode avalanche photo-diode: Tests on simple synthetic scenarios; pp. 22–31.

Hwang S., Kim S., Lee I. Modeling and Simulation for Defense Systems and Applications VI. Volume 8060. SPIE; Bellingham, WA, USA: 2011. Efficient geometric computation for ladar simulation; pp. 190–197.

Alsadik B. Ideal Angular Orientation of Selected 64-Channel Multi Beam Lidars for Mobile Mapping Systems. Remote Sens. 2020;12:510. doi: 10.3390/rs12030510. DOI

Hussmann S. Automation in Agriculture; Chapter Robotic Harvesting of Fruiting Vegetables: A Simulation Approach in V-REP, ROS and MATLAB. IntechOpen; London, UK: 2018. Automation in Agriculture: Securing Food Supplies for Future Generations.

Kumar S., Savur C., Sahin F. Dynamic Awareness of an Industrial Robotic Arm Using Time-of-Flight Laser-Ranging Sensors; Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC); Miyazaki, Japan. 7–10 October 2018; pp. 2850–2857. DOI

Ira_laser_tools: A ROS LaserScan Manipulation Toolbox. [(accessed on 5 July 2021)]; Available online: https://arxiv.org/pdf/1411.1086.pdf.

Perez-Gonzalez J., Luna-Madrigal F., Pi na-Ramirez O. Deep Learning Point Cloud Registration based on Distance Features. IEEE Lat. Am. Trans. 2019;17:2053–2060. doi: 10.1109/TLA.2019.9011551. DOI

Jolliffe I. Principal Component Analysis. Springer; Berlin, Germany: 2002.

Schnabel R., Wahl R., Klein R. Efficient RANSAC for Point-Cloud Shape Detection. Comput. Graph. Forum. 2007;26:214–226. doi: 10.1111/j.1467-8659.2007.01016.x. DOI

Straková E., Lukáš D., Bobovský Z., Kot T., Mihola M., Novák P. Matching Point Clouds with STL Models by Using the Principle Component Analysis and a Decomposition into Geometric Primitives. Appl. Sci. 2021;11:2268. doi: 10.3390/app11052268. DOI

Horaud R., Hansard M., Evangelidis G., Ménier C. An overview of depth cameras and range scanners based on time-of-flight technologies. Mach. Vision Appl. 2016;27:1005–1020. doi: 10.1007/s00138-016-0784-4. DOI

Direct3D 11 Graphics. [(accessed on 5 July 2021)];2018 Available online: https://docs.microsoft.com/cs-cz/windows/win32/direct3d11/

DirectXMath. [(accessed on 5 July 2021)];2019 Available online: https://docs.microsoft.com/cs-cz/windows/win32/dxmath/

Möller T., Trumbore B. Fast, Minimum Storage Ray-Triangle Intersection. J. Graph. Tools. 1997;2:21–28. doi: 10.1080/10867651.1997.10487468. DOI

Ye Y., Song Z. An accurate 3D point cloud registration approach for the turntable-based 3D scanning system; Proceedings of the 2015 IEEE International Conference on Information and Automation; Lijiang, China. 8–10 August 2015; pp. 982–986.

Li J., Chen M., Jin X., Chen Y., Dai Z., Ou Z., Tang Q. Calibration of a multiple axes 3-D laser scanning system consisting of robot, portable laser scanner and turntable. Optik. 2011;122:324–329. doi: 10.1016/j.ijleo.2010.02.014. DOI

Kaufman A., Cohen D., Yagel R. Volume Graphics: Fundamentals of Voxelization. IEEE Comput. 1993;26:51–64. doi: 10.1109/MC.1993.274942. DOI

Chakravorty D. STL File Format (3D Printing); 2019. [(accessed on 5 July 2021)]; Available online: https://all3dp.com/what-is-stl-file-format-extension-3d-printing/

Hammad Mian S., Abdul Mannan M., Al-Ahmari M.A. The influence of surface topology on the quality of the point cloud data acquired with laser line scanning probe. Sens. Rev. 2014;34:255–265. doi: 10.1108/SR-01-2013-611. DOI

Heczko D., Oščádal P., Kot T., Huczala D., Semjon J., Bobovský Z. Increasing the Reliability of Data Collection of Laser Line Triangulation Sensor by Proper Placement of the Sensor. Sensors. 2021;21:2890. doi: 10.3390/s21082890. PubMed DOI PMC

Enhancing Data Collection Through Optimization of Laser Line Triangulation Sensor Settings and Positioning

Finding the Optimal Pose of 2D LLT Sensors to Improve Object Pose Estimation