Taşınabilir Lazer Tarama Sistemleri ile Farklı Alanlarda Doğruluk Analizi

Yıl 2022, , 1075 - 1086, 27.10.2022

Zübeyir Bilal Çakmak , Burak Akpınar , Mahmut Oğuz Selbesoğlu

https://doi.org/10.35414/akufemubid.1139569

Öz

Teknolojideki gelişmelere paralel olarak mobil lidar sistemlerinin kullanım alanları günümüzde hızla artmaktadır. Özellikle GNSS ile konum belirlemenin mümkün olmadığı kapalı alanlarda SLAM algoritmalarının sağladığı avantajlar ile haritalama çalışmaları yüksek doğrulukta hızda yapılabilmektedir. Bu çalışmada, geliştirilen bir mobil lidar sistemi ile ağaçlık alan, kapalı alan ve dış mekanda yapılan ölçmeler sonucunda söz konusu alanların üç boyutlu modelleri üretilmiş ve üretilen modellerin doğruluk analizi yapılarak, GNSS ile konumlamanın mümkün olmadığı durumlarda mobil lidar sistemlerinin doğrulukları araştırılmıştır. Yapılan testler sonucunda geliştirilen mobil lidar sistemi ile ağaçlık alanlar, kapalı alanlar ve dış mekanlarda yapılan çalışmalar için sırasıyla ±2.1 cm, ±2.4 cm ve ±3.0 cm standart sapma değerleri elde edilmiştir. Bu sonuçlara göre sistemin orman envanterinin belirlenmesi çalışmalarında, kapalı ve açık alanlarda yapılacak mimari rölöve vb çalışmalarda kullanılabileceği öngörülmektedir.

Anahtar Kelimeler

Mobil Lidar Sistemi, Taşınabilir Lazer Tarama Sistemi, üç boyutlu modelleme, Lidar, SLAM

Destekleyen Kurum

BeGeo Yazılım Teknolojileri A.Ş

Teşekkür

Bu çalışmanın gerçekleştirilmesi aşamasında sağladığı donanım desteği için BeGeo Yazılım Teknolojileri A.Ş.’ ne teşekkür ederiz.

Accuracy Assessment of Mobile Lidar System in Different Environments

Öz

Depending on the developments in technology, the usage areas of mobile lidar systems are increasing rapidly today. With the advantages of SLAM algorithms, mapping studies can be performed with high accuracy, especially in areas where positioning is not possible with GNSS. In this study, with a developed mobile lidar system, three-dimensional models of the different areas were produced as a result of measurements made in woodland, indoor and outdoor areas, and the accuracy of the produced models was investigated in cases where positioning with GNSS was not possible. As a result of the tests, ±2.1 cm, ±2.4 cm and ±3.0 cm standard deviation values were obtained, respectively, for the studies carried out in woodland, indoor areas and outdoors with the mobile lidar system developed.

Anahtar Kelimeler

Lidar, Mobile Lidar System, simultaneous localization and mapping, SLAM, 3-dimensional modelling

Kaynakça

• Arp, H. and Tranarg, C., 1982. Mapping in tropical forests: a new approach using the laser APR [Airborne Profile Recorder]. Photogrammetric Engineering and Remote Sensing, 48.
• Bailey, T. Nieto, J. Guivant, J. Stevens, M. Nebot, E. 2006. Consistency of the EKF-SLAM Algorithm, IEEE/RSJ International Conference on Intelligent Robots and Systems, 09-15 October 2006, Beijing, China, doi: 10.1109/IROS.2006.281644.
• Cabo, C. Del Pozo, S. Rodriuez-Gonzalvez, P. Ordonez, C. Gonzalez-Aguilera, D., 2018. Comparing terrestrial laser scanning (TLS) and wearable laser scanning (WLS) for individual tree modeling at plot level. Remote Sensing. 10(4), 1-16, doi: 10.3390/rs10040540.
• Glennie, C., 2009. Kinematic terrestrial light-detection and ranging system for scanning. Transportation research record, 2105(1), 135–141, doi: 10.3141/2105-17.
• Gollob, C. Ritter, T. Nothdurft, A., 2020. Forest inventory with long range and high-speed personal laser scanning (PLS) and simultaneous localization and mapping (SLAM) technology. Remote Sensing, 12(9), 1-43, doi: 10.3390/rs12091509.
• Grisetti, G. Kümmerle, R. Stachniss, C. Burgard, W. A., 2010. Tutorial on Graph‐Based SLAM. IEEE Intelligent Transportation Systems Magazine, 2, 31–43, doi: 10.1109/MITS.2010.939925.
• Harding, D.J. Bufton, J.L. Frawley, J.J., 1994. Satellite laser altimetry of terrestrial topography: vertical accuracy as a function of surface slope, roughness, and cloud cover. IEEE Transactions on Geoscience and Remote Sensing, 32, 329-339, doi: 10.1109/36.295048.
• Hickman, G.D. and Hogg, J.E., 1969. Application of an airborne pulsed laser for near shore bathymetric measurements. Remote Sensing of Environment, 1, 47-58, doi: 10.1016/S0034-4257(69)90088-1
• Koide, K. Miura, J. Menegatti, E., 2019. A portable three‐dimensional LIDARbased system for long‐term and widearea people behavior measurement. International Journal of Advanced Robotic Systems, 16, 1-16, doi: 10.1177/1729881419841532.
• Koide, K. Miura, J. Yokozuka, M. Oishi, S. Banno, A. 2020. Interactive 3D Graph SLAM for Map Correction. IEEE Robotics and Automation Letters, 6, 40–47, doi:10.1109/LRA.2020.3028828.
• Lefsky, M.A. Harding, D.J. Keller, M. Cohen, W.B. Carabajal, C.C. Espirito-Santo, F.D.B. Hunter, M.O. Oliveira, R. 2005. Estimates of forest canopy height and aboveground biomass using ICESat. Geophysical research letters, 32, 1-4, doi: 0.1029/2005GL023971.
• Liang, X. Hyyppa, J. Kukko, A. Kaartinen, H. Jaakkola, A. Yu, X. 2014. The Use of a Mobile Laser Scanning System for Mapping Large Forest Plots, IEEE Geoscience and Remote Sensing Letters, 11, 1504-1508, doi: 10.1109/LGRS.2013.2297418.
• Mossmann, F. and Stiller, C., 2011. Velodyne SLAM. In Proceedings of the IEEE Intelligent Vehicles Symposium (IV), 5–9 June 2011, Baden‐Baden, Germany.
• Nelson, R. Krabill, W. Tonelli, J., 1988. Estimating forest biomass and volume using airborne laser data. Remote Sensing of Environment, 24, 247-267, doi: 10.1016/0034-4257(88)90028-4.
• Nüchter, A. Lingemaan, K. Hertzberg, J. Surmann, H., 2007. 6D SLAM—3D Mapping Outdoor Environments. Journal of Field Robotics, 24, 699–722, doi: 10.1002/rob.20209.
• Pierzchala, M. Giguere, P. Astrup, R., 2018. Mapping forests using an unmanned ground vehicle with 3D LiDAR and graph-SLAM, Computers and Electronics in Agriculture, 145, 217-225, doi: 0.1016/j.compag.2017.12.034.
• Sepasgozar, S. Lim, S. Shirwzhan, S., 2014. Implementation of Rapid As-built Building Information Modeling Using Mobile LiDAR, Construction Research Congress 2014, Atlanta, USA, doi: 10.1061/9780784413517.022.
• Sobczak, L. Filus, K. Domanski, A. Domanska, J., 2021. LiDAR Point Cloud Generation for SLAM Algorithm Evaluation. Sensors, 21, 1-21, doi: 10.3390/s21103313.
• Stefano, F.D. Chiappini, S. Gorreja, A. Balestra, M. Pierdicca, R., 2021. Mobile 3D scan LiDAR: a literature review. Geomatics, Natural Hazards and Risk, 12(1), 2387-2429, doi: 10.1080/19475705.2021.1964617.
• Velodyne LiDAR. 2019. VLP-16 User Manual, https://velodynelidar.com/wpcontent/uploads/2019/12/63-9243-Rev-E-VLP-16-User-Manual. (Online, 2019).
• Wang, Z. Huang, S. Dissanayake, G., 2011. Simultaneous Localization and Mapping Exactly Sparse Information Filters, New Frontiers in Robotics; World Scientific: Singapore, 3. ISBN: 978‐981‐4350‐31‐0, doi: 10.1142/8145.
• Wang, K. Zhou, J. Zhang, W. Zhang, B., 2021. Mobile LiDAR Scanning System Combined with Canopy Morphology Extracting Methods for Tree Crown Parameters Evaluation in Orchards. Sensors, 21, 1-15, doi:10.3390/s21020339.
• Yang, J.C. Lin, C.J. You, B.Y. Yan, Y.L. Cheng, T.H., 2021. RTLIO: Real‐Time LiDAR‐Inertial Odometry and Mapping for UAVs. Sensors, 21, 1-21, doi: 10.3390/s21123955.
• Zhang, J. Singh, S., 2017. Low‐drift and real‐time lidar odometry and mapping. Autonomous Robots, 41, 401–416, doi: 10.1007/s10514-016-9548-2.