{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T20:41:37Z","timestamp":1779223297325,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T00:00:00Z","timestamp":1607990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Three-dimensional (3D) imaging technologies have been increasingly explored in academia and the industrial sector, especially the ones yielding point clouds. However, obtaining these data can still be expensive and time-consuming, reducing the efficiency of procedures dependent on large datasets, such as the generation of data for machine learning training, forest canopy calculation, and subsea survey. A trending solution is developing simulators for imaging systems, performing the virtual scanning of the digital world, and generating synthetic point clouds from the targets. This work presents a guideline for the development of modular Light Detection and Ranging (LiDAR) system simulators based on parallel raycasting algorithms, with its sensor modeled by metrological parameters and error models. A procedure for calibrating the sensor is also presented, based on comparing with the measurements made by a commercial LiDAR sensor. The sensor simulator developed as a case study resulted in a robust generation of synthetic point clouds in different scenarios, enabling the creation of datasets for use in concept tests, combining real and virtual data, among other applications.<\/jats:p>","DOI":"10.3390\/s20247186","type":"journal-article","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T09:12:57Z","timestamp":1608023577000},"page":"7186","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Development and Validation of LiDAR Sensor Simulators Based on Parallel Raycasting"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3545-3811","authenticated-orcid":false,"given":"Guilherme Ferreira","family":"Gusm\u00e3o","sequence":"first","affiliation":[{"name":"Postgraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro, Rua Marqu\u00eas de S\u00e3o Vicente, 225, G\u00e1vea, Rio de Janeiro 22451-900, Brazil"},{"name":"Tecgraf Institute, Pontifical Catholic University of Rio de Janeiro, Rua Marqu\u00eas de S\u00e3o Vicente, 225, G\u00e1vea, Rio de Janeiro 22451-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0753-9044","authenticated-orcid":false,"given":"Carlos Roberto Hall","family":"Barbosa","sequence":"additional","affiliation":[{"name":"Postgraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro, Rua Marqu\u00eas de S\u00e3o Vicente, 225, G\u00e1vea, Rio de Janeiro 22451-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7279-1823","authenticated-orcid":false,"given":"Alberto Barbosa","family":"Raposo","sequence":"additional","affiliation":[{"name":"Tecgraf Institute, Pontifical Catholic University of Rio de Janeiro, Rua Marqu\u00eas de S\u00e3o Vicente, 225, G\u00e1vea, Rio de Janeiro 22451-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1117\/1.1631921","article-title":"Review of 20 years of range sensor development","volume":"13","author":"Blais","year":"2004","journal-title":"J. Electron. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/7361.987055","article-title":"Coaxial range measurement\u2014Current trends for mobile robotic applications","volume":"2","author":"Adams","year":"2002","journal-title":"IEEE Sens. J."},{"key":"ref_3","unstructured":"Chow, J.C.K. (2014). Multi-Sensor Integration for Indoor 3D Reconstruction. [Ph.D. Thesis, University of Calgary]."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hanke, T., Schaermann, A., Geiger, M., Rauch, A., Schneider, S.-A., and Biebl, E. (2017, January 16\u201319). Generation and validation of virtual point cloud data for automated driving systems. Proceedings of the IEEE 20th International Conference on Intelligent Transportation Systems, Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317864"},{"key":"ref_5","unstructured":"(2019, December 11). Velodyne Lidar. Available online: https:\/\/velodynelidar.com\/index.html."},{"key":"ref_6","unstructured":"\u00d6hman, N. (2018). Simulation of LiDAR Data for Forestry Applications. [Master\u2019s Thesis, Ume\u00e5 University]."},{"key":"ref_7","unstructured":"(2019, December 12). NASA, Available online: https:\/\/www.nasa.gov\/feature\/goddard\/2018\/3d-view-of-amazon-forests-captures-effects-of-el-ni-o-drought."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.agrformet.2013.09.005","article-title":"On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrial LiDAR","volume":"184","author":"Baldocchi","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.3390\/rs3061104","article-title":"Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning","volume":"3","author":"Remondino","year":"2011","journal-title":"Remote Sens."},{"key":"ref_10","unstructured":"Nuttens, T., De Maeyer, P., De Wulf, A., Goossens, R., and Stal, C. (June, January 30). Comparison of 3D accuracy of terrestrial laser scanning and digital photogrammetry: An archaeological case study. Proceedings of the 31st EARSeL Symposium: Remote Sensing and Geoinformation Not Only for Scientific Cooperation, Prague, Czech Republic."},{"key":"ref_11","first-page":"85","article-title":"Airborne LiDAR acquisition, post-processing and accuracy-checking for a 3D WebGIS of Copan, Honduras","volume":"5","author":"Remondino","year":"2016","journal-title":"J. Archaeol. Sci. Rep."},{"key":"ref_12","first-page":"31525","article-title":"Refraction correction of airborne LiDAR bathymetry based on sea surface profile and ray tracing","volume":"15","year":"2015","journal-title":"Sensors"},{"key":"ref_13","first-page":"6141","article-title":"Optical sensors and methods for underwater 3D reconstruction","volume":"55","author":"Fanlin","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","unstructured":"(2019, December 11). 3D at Depth. Available online: https:\/\/www.3datdepth.com\/."},{"key":"ref_15","unstructured":"Tallavajhula, A. Lidar Simulation for Robotic Application Development: Modeling and Evaluation. [Ph.D. Thesis, Carnegie Mellon University]."},{"key":"ref_16","unstructured":"Lohani, B., and Mishra, R.K. (2007, January 12\u201314). Generating LIDAR data in laboratory: LIDAR simulator. Proceedings of the ISPRS Workshop on Laser Scanning and SilviLaser, Espoo, Finland."},{"key":"ref_17","unstructured":"(2020, January 16). Marine Technology News: Subsea Wreck Brought to Life by Lasers. Available online: https:\/\/www.marinetechnologynews.com\/news\/subsea-wreck-brought-lasers-587916."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2671","DOI":"10.1109\/TIM.2019.2906416","article-title":"Automatic Generation of Synthetic LiDAR Point Clouds for 3-D Data Analysis","volume":"68","author":"Wang","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_19","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_20","unstructured":"Gao, B., Pan, Y., Li, C., Geng, S., and Zhao, H. (2020). Are we hungry for 3D LiDAR data for semantic segmentation?. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1778765.1778803","article-title":"OptiX: A general purpose ray tracing engine","volume":"29","author":"Parker","year":"2010","journal-title":"ACM Trans. Graph."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.1109\/LRA.2020.2969927","article-title":"Augmented LiDAR Simulator for Autonomous Driving","volume":"5","author":"Fang","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Manivasagam, S., Wang, S., Wong, K., Zeng, W., Sazanovich, M., Tan, S., Yang, B., Ma, W.-C., and Urtasun, R. (2020, January 16\u201318). LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01118"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhao, S., Wang, Y., Li, B., Wu, B., Gao, Y., Xu, P., Darrell, T., and Keutzer, K. (2020). ePointDA: An end-to-end simulation-to-real domain adaptation framework for LiDAR point cloud segmentation. arXiv.","DOI":"10.1609\/aaai.v35i4.16464"},{"key":"ref_26","unstructured":"Hadj-Bachir, M., and de Souza, P. (2019). LIDAR Sensor Simulation in Adverse Weather Condition for Driving Assistance Development, Version 1, HAL."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Boucher, P.B., Hancock, S., Orwig, D.A., Duncanson, L., Armston, J., Tang, H., Krause, K., Cook, B., Paynter, I., and Li, Z. (2020). Detecting change in forest structure with simulated GEDI lidar waveforms: A case study of the Hemlock Woolly Adelgid (HWA; Adelges tsugae) infestation. Remote Sens., 12.","DOI":"10.3390\/rs12081304"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107610","DOI":"10.1016\/j.agrformet.2019.06.009","article-title":"Simulation of multi-platform LiDAR for assessing total leaf area in tree crowns","volume":"276","author":"Yun","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_29","unstructured":"Gusm\u00e3o, G.F. (2020). Development and Validation of a LiDAR Virtual Sensor. [Master\u2019s Thesis, Pontifical Catholic University of Rio de Janeiro]."},{"key":"ref_30","unstructured":"Xiangyu, Y., Bichen, W., Seshia, S., Keutzer, K., and Sangiovanni-Vicentelli, A. (2018, January 11\u201314). A LiDAR point cloud generator: From a virtual world to autonomous driving. Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, Yokohama, Japan."},{"key":"ref_31","unstructured":"(2019, June 23). Unity3D Manual. Available online: https:\/\/docs.unity3d.com\/Manual\/index.html."},{"key":"ref_32","unstructured":"(2019, June 23). Hokuyo: Rangefinder URG-04LX-UG01. Available online: https:\/\/www.hokuyo-aut.jp\/search\/single.php?serial=166."},{"key":"ref_33","unstructured":"(2019, December 28). ALICE Project-Team: Geogram. Available online: https:\/\/www.unity.com\/."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Detry, R., Koch, J., Pailevanian, T., Garret, M., Levine, D., Yahnker, C., and Gildner, M. (2018, January 28\u201331). Turbid-water subsea infrastructure 3D reconstruction with assisted stereo. Proceedings of the 2018 OCEANS-MTS\/IEEE Kobe Techno-Oceans (OTO), Kobe, Japan.","DOI":"10.1109\/OCEANSKOBE.2018.8559091"},{"key":"ref_35","unstructured":"(2019, February 06). Free 3D. Available online: https:\/\/free3d.com\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7186\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:45:18Z","timestamp":1760179518000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7186"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,15]]},"references-count":35,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20247186"],"URL":"https:\/\/doi.org\/10.3390\/s20247186","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,15]]}}}