Development of a bulk material volume estimation system using automatic moving rail LiDAR technology

Authors

DOI:

https://doi.org/10.58190/ijamec.2024.97

Keywords:

Bulk material, Volume estimation, LiDAR technology, Point cloud data

Abstract

This paper focuses on developing a bulk material volume estimation system employing automatic moving rail optical distance measuring technology. The research objective is to devise a system capable of estimating warehouse bulk material volumes utilizing point cloud data. Additionally, the research proposes guidelines for enhancing efficiency in bulk material volume estimation processes. A prototype system was developed and tested using dry rice husk with a humidity level of 15% as the sample material. The testing environment comprised a laboratory with dimensions of 36 square meters and a height of 3 meters, wherein the sample material was arranged in a cone shape with a volume of 1 cubic meter. The system was designed to test with movement speed range from 2 to 30 centimeters per second, and the scanning angles of 0, 45, and 90 degrees. Statistical principles were applied to analyze the collected data, determining averages, and comparing results with actual data to assess accuracy and precision in volumetric measurements. Furthermore, the research evaluated the advantages and disadvantages of alternative tools for bulk material volume estimation in comparison to the developed system, considering factors such as data collection duration, operational costs, and safety measures. Experimental results revealed that at a scanning angle of 90 degrees and a moving speed of 20 centimeters per second achieve a volumetric data accuracy of 97%.

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References

Hugenholtz, C.H.; Walker, J.; Brown, O.; Myshak, S. Earthwork Volumetrics with an Unmanned Aerial Vehicle and Softcopy Photogrammetry. J. Surv. Eng. 2015, 141, 06014003.

Luo, Y.; Chen, J.; Xi, W.; Zhao, P.; Qiao, X.; Deng, X.; Liu, Q. Analysis of Tunnel Displacement Accuracy with Total Station. Meas.J. Int. Meas. Confed. 2016, 83, 29–37.

Zhu, J.; Yang, J.; Fan, J.; Danni, A.; Jiang, Y.; Song, H.; Wang, Y. Accurate Measurement of Granary Stockpile Volume Based on Fast Registration of Multi-Station Scans. Remote Sens. Lett. 2018, 9, 569–577.

Alsayed, Ahmad, and Mostafa RA Nabawy. "Stockpile Volume Estimation in Open and Confined Environments: A Review." Drones 7.8 (2023): 537.

Kumar, C.; Mathur, Y.; Jannesari, A. Efficient Volume Estimation for Dynamic Environments Using Deep Learning on the Edge. In Proceedings of the 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Lyon, France, 30 May–3 June 2022; pp. 995–1002.

Gago, Reynaldo M., Matheus YA Pereira, and Guilherme AS Pereira. "An aerial robotic system for inventory of stockpile warehouses." Engineering Reports 3.9 (2021): e12396.

Alsayed, Ahmad, et al. "An Autonomous Mapping Approach for Confined Spaces Using Flying Robots." Towards Autonomous Robotic Systems: 22nd Annual Conference, TAROS 2021, Lincoln, UK, September 8–10, 2021, Proceedings 22. Springer International Publishing, 2021.

Alsayed, Ahmad, et al. "Real-time scan matching for indoor mapping with a drone." AIAA SCITECH 2022 Forum. 2022.

Alsayed, Ahmad, and Mostafa RA Nabawy. "Indoor stockpile reconstruction using drone-borne actuated single-point lidars." Drones 6.12 (2022): 386.

Alsayed, Ahmad, Mostafa R. Nabawy, and Farshad Arvin. "Autonomous Aerial Mapping using a Swarm of Unmanned Aerial Vehicles." AIAA AVIATION 2022 Forum. 2022.

Mahlberg, Justin Anthony, et al. "Salt Stockpile Inventory Management Using LiDAR Volumetric Measurements." Remote Sensing 14.19 (2022): 4802.

Liu, Jidong, et al. "An image-aided sparse point cloud registration strategy for managing stockpiles in dome storage facilities." Remote Sensing 15.2 (2023): 504.

de Lima, Duan Pelissaro, and Guilherme Holsbach Costa. "On the Stockpiles Volume Measurement Using a 2D Scanner." 2021 5th International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT). IEEE, 2021.

Zhao, Shi, et al. "Stockpile modelling using mobile laser scanner for quality grade control in stockpile management." 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, 2012.

Xu, Zhihua, et al. "A Sliding System Based on Single-Pulse Scanner and Rangefinder for Pile Inventory." IEEE Geoscience and Remote Sensing Letters 19 (2022): 1-5.

Chang, Daofang, Houjun Lu, and Weijian Mi. "Bulk terminal stockpile automatic modeling based on 3D scanning technology." 2010 International Conference on Future Information Technology and Management Engineering. Vol. 1. IEEE, 2010.

Zhang, Xiaohu, et al. "Projection-aided videometric method for shape measurement of large-scale bulk material stockpile." Applied Optics 50.26 (2011): 5178-5184.

Manish, Raja, et al. "Image-aided LiDAR mapping platform and data processing strategy for stockpile volume estimation." Remote Sensing 14.1 (2022): 231.

Rusu, Radu Bogdan, and Steve Cousins. "3d is here: Point cloud library (pcl)." 2011 IEEE international conference on robotics and automation. IEEE, 2011.

Yang, Xingyu, Yuchun Huang, and Qiulan Zhang. "Automatic stockpile extraction and measurement using 3D point cloud and multi-scale directional curvature." Remote Sensing 12.6 (2020): 960.

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Published

30-06-2024

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Research Articles

How to Cite

[1]
“Development of a bulk material volume estimation system using automatic moving rail LiDAR technology”, J. Appl. Methods Electron. Comput., vol. 12, no. 2, pp. 48–53, Jun. 2024, doi: 10.58190/ijamec.2024.97.

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