Investigation of geometric object and indoor mapping capacity of Apple iPhone 12 Pro LiDAR

Year 2023, Volume: 5 Issue: 2, 82 - 89, 15.12.2023

Mehmet Akif Günen , İlker Erkan , Şener Aliyazıcıoğlu , Cavit Kumaş

https://doi.org/10.53093/mephoj.1354998

Cited By: 2

Abstract

LiDAR (light detection and ranging) sensors use laser beams to calculate distances in the surroundings. These sensors can be applied to a wide range of tasks, and they are frequently helpful in tasks like building 3D maps, navigating airplanes, robots, conducting mining operations, and automated driving. High-resolution distance measurements are taken by LiDAR sensors, but they also gather environmental data. This information aids in locating, identifying, and quantifying things and their surroundings. The iPhone 12 Pro, which Apple released in 2020, was evaluated for accuracy with various geometric shapes and its capacity to recognize indoor environments. Free of charge 3D Scanner and the Clirio Scan application were employed in this situation. However, it was found that the root mean square error and mean error in indoor mapping were ±1.41 cm and -0.56 cm in 3D Scanner and ±3.94 cm and -0.60 cm in the Clirio Scan application, respectively, despite the findings obtained showing low accuracy in scanning small geometric objects due to the scanning difficulty. Clirio does not reject the null hypothesis in the t-test that was conducted. The accuracy of the LiDAR sensor in indoor mapping has been shown to be more promising than that of small items. In order to evaluate the reliability and reusability of the indoor mapping application according to reference measurements, intraclass correlation test was performed and the results were determined to be reliable.

Keywords

Iphone 12 Pro, Point Cloud, LiDAR, Mapping

References

• Günen, M. A., & Beşdok, E. (2021). Comparison of point cloud filtering methods with data acquired by photogrammetric method and RGB-D sensors. International Journal of Engineering and Geosciences, 6(3), 125-135. https://doi.org/10.26833/ijeg.731129
• Guo, B., Zhang, Y., Gao, J., Li, C., & Hu, Y. (2022, September). SGLBP: Subgraph‐based local binary patterns for feature extraction on point clouds. Computer Graphics Forum, 41(6), 51-66. https://doi.org/10.1111/cgf.14500
• Seyfeli, S., & OK, A. (2022). Classification of mobile laser scanning data with geometric features and cylindrical neighborhood. Baltic Journal of Modern Computing, 10(2), 209-223. https://doi.org/10.22364/bjmc.2022.10.2.08
• Silva, M. F., Green, A., Morales, J., Meyerhofer, P., Yang, Y., Figueiredo, E., ... & Mascareñas, D. (2022). 3D structural vibration identification from dynamic point clouds. Mechanical Systems and Signal Processing, 166, 108352. https://doi.org/10.1016/j.ymssp.2021.108352
• Nex, F., & Rinaudo, F. (2009). New integration approach of photogrammetric and LIDAR techniques for architectural surveys. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(3), 12-17
• Bahirat, K., & Prabhakaran, B. (2017). A study on lidar data forensics. IEEE International Conference on Multimedia and Expo (ICME), 679-684. https://doi.org/10.1109/ICME.2017.8019395
• Almeida, D. R. A. D., Stark, S. C., Chazdon, R., Nelson, B. W., César, R. G., Meli, P., ... & Brancalion, P. H. S. (2019). The effectiveness of lidar remote sensing for monitoring forest cover attributes and landscape restoration. Forest Ecology and Management, 438, 34-43. https://doi.org/10.1016/j.foreco.2019.02.002
• Lozić, E., & Štular, B. (2021). Documentation of archaeology-specific workflow for airborne LiDAR data processing. Geosciences, 11(1), 26. https://doi.org/10.3390/geosciences11010026
• Leottau, D. L., Vallejos, P., & del Solar, J. R. (2018). LIDAR-based displacement estimation in mining applications. 6th International Congress on Automation in Mining. 1-9
• Froese, C. R., & Mei, S. (2008). Mapping and monitoring coal mine subsidence using LiDAR and InSAR. GeoEdmonton, 8, 1127-1133.
• Kekeç, B., Bilim, N., Karakaya, E., & Ghiloufi, D. (2021). Applications of terrestrial laser scanning (TLS) in mining: A review. Türkiye Lidar Dergisi, 3(1), 31-38. https://doi.org/10.51946/melid.927270
• Murray, X., Apan, A., Deo, R., & Maraseni, T. (2022). Rapid assessment of mine rehabilitation areas with airborne LiDAR and deep learning: bauxite strip mining in Queensland, Australia. Geocarto International, 37(26), 11223-11252. https://doi.org/10.1080/10106049.2022.2048902
• Günen, M. A., Çoruh, L., & Beşdok, E. (2017). Oyun dünyasında model ve doku üretiminde fotogrametri kullanımı. Geomatik, 2(2), 86-93. https://doi.org/10.29128/geomatik.318319
• Horaud, R., Hansard, M., Evangelidis, G., & Ménier, C. (2016). An overview of depth cameras and range scanners based on time-of-flight technologies. Machine vision and applications, 27(7), 1005-1020. https://doi.org/10.1007/s00138-016-0784-4
• Zhang, P., He, H., Wang, Y., Liu, Y., Lin, H., Guo, L., & Yang, W. (2022). 3D urban buildings extraction based on airborne lidar and photogrammetric point cloud fusion according to U-Net deep learning model segmentation. IEEE Access, 10, 20889-20897. https://doi.org/10.1109/ACCESS.2022.3152744
• Zeybek, M., & Ediz, D. (2022). Detection of road distress with mobile phone LiDAR sensors. Advanced Lidar, 2(2), 48-53.
• Karkinli, A. E. (2023). Detection of object boundary from point cloud by using multi-population based differential evolution algorithm. Neural Computing and Applications, 35(7), 5193-5206. https://doi.org/10.1007/s00521-022-07969-w
• Aliyazıcıoğlu, Ş., Öztürk, K. F., & Günen, M. A. (2023). Analysis of Gümüşhane-Trabzon highway slope static and dynamic behavior using point cloud data. Advanced Lidar, 3(1), 70-75.
• Günen, M. A., Kesikoğlu, A., Karkinli, A. E., & Beşdok, E. (2017). RGB-D sensörler ile iç mekan haritalaması. International Artificial Intelligence and Data Processing Symposium (IDAP), 1-6. https://doi.org/10.1109/IDAP.2017.8090220
• Kadkhodamohammadi, A., Gangi, A., de Mathelin, M., & Padoy, N. (2017). A multi-view RGB-D approach for human pose estimation in operating rooms. IEEE winter conference on applications of computer vision (WACV), 363-372. https://doi.org/10.1109/WACV.2017.47
• Vogt, M., Rips, A., & Emmelmann, C. (2021). Comparison of iPad Pro®’s LiDAR and TrueDepth capabilities with an industrial 3D scanning solution. Technologies, 9(2), 25. https://doi.org/10.3390/technologies9020025
• Teppati Losè, L., Spreafico, A., Chiabrando, F., & Giulio Tonolo, F. (2022). Apple LiDAR Sensor for 3D Surveying: Tests and Results in the Cultural Heritage Domain. Remote Sensing, 14(17), 4157. https://doi.org/10.3390/rs14174157
• Kuçak, R. A., Erol, S., & Alkan, R. M. (2023). iPad Pro LiDAR sensörünün profesyonel bir yersel lazer tarayıcı ile karşılaştırmalı performans analizi. Geomatik, 8(1), 35-41. https://doi.org/10.29128/geomatik.1105048
• https://support.apple.com/kb/SP831?locale=en_US
• https://developer.apple.com/augmented-reality/arkit/
• Taubin, G. (1995). A signal processing approach to fair surface design. in Proceedings of the 22nd annual conference on Computer graphics and interactive techniques. 351-358