The applicability of a handheld mobile laser scanner (MLS) in oil shale mine surveys and subsequent three-dimensional modelling of post-extracted surfaces is assessed. Recommendations for optimizing the acquisition and processing of MLS data and visualization of the results are given. The resulting surface geometry accuracy is validated via terrestrial laser scanner (TLS) reference data. Typical discrepancies between TLS and MLS data points remain within 2 and 5 cm in horizontal and vertical directions, respectively. The area of pillars and volumes of the extracted material are estimated by data analysis. The results are compared with those of the conventional mining survey. The detected discrepancies evidence that the laser scanning results provide a realistic outcome due to the evenly and densely spaced points within the point cloud. The result discrepancies between the tested surveying technologies are small and fully satisfy contemporary accuracy requirements. However, the handheld mobile laser scanning appears to be the most suitable method for underground mining surveys. The survey results enable reduction of mining losses and improvementt of the design of mining geometry.
1. Sobak, M., Ellmann, A., Mill, T. Terrestrial laser scanning assessment of generalization errors in conventional topographic surveys. Geodesy Cartogr., 2015, 41(1), 15‒24.
https://doi.org/10.3846/20296991.2015.1029755
2. Wunderlich, T. A. Terrestrial laser scanners – an important step towards construction information. FIG Working Week, Paris, France, April 13–17, 2003, 1–11.
3. Murphy, M., McGovern, E., Pavia, S. Historic building information modelling – adding intelligence to laser and image based surveys of European classical architecture. ISPRS J. Photogramm. Remote Sens., 2013, 76, 89–102.
https://doi.org/10.1016/j.isprsjprs.2012.11.006
4. Varbla, S., Puust, R., Ellmann, A. Accuracy assessment of RTK-GNSS equipped UAV conducted as-built surveys for construction site modelling. Surv. Rev., 2020.
https://doi.org/10.1080/00396265.2020.1830544
5. Mill, T., Ellmann, A. Assessment of along-normal uncertainties for application to terrestrial laser scanning surveys of engineering structures. Surv. Rev., 2019, 51(364), 1−16.
https://doi.org/10.1080/00396265.2017.1361565
6. Mill, T., Ellmann, A., Aavik, A., Horemuz, M., Sillamäe, S. Determining ranges and spatial distribution of road frost heave by terrestrial laser scanning. Balt. J. Road Bridge Eng., 2014, 9(3), 225−234.
https://doi.org/10.3846/bjrbe.2014.28
7. Julge, K., Vajakas, T., Ellmann, A. Performance analysis of a compact and low-cost mapping-grade mobile laser scanning system. J. Appl. Remote Sens., 2017, 11(4), 044003.
https://doi.org/10.1117/1.JRS.11.044003
8. Lõhmus, H., Ellmann, A., Märdla, S., Idnurm, S. Terrestrial laser scanning for the monitoring of bridge load tests – two case studies. Surv. Rev., 2017, 50(360), 270−284.
https://doi.org/10.1080/00396265.2016.1266117
9. Julge, K., Ellmann, A., Köök, R. Unmanned aerial vehicle surveying for monitoring road construction earthworks. Balt. J. Road Bridge Eng., 2019, 14(1), 1−17.
https://doi.org/10.7250/bjrbe.2019-14.430
10. Zlot, R., Bosse, M. Efficient large-scale 3D mobile mapping and surface reconstruction of an underground mine. Field and Service Robotics, Results of the 8th International Conference, Tohoku University/Matsushima, Japan, July 16−19, 2012.
https://doi.org/10.1007/978-3-642-40686-7_32
11. Eesti Energia AS. Applied Research on Improving Effectiveness of Mine-Surveyor Works Using Remote Sensing Methods (Procurement #195308). Tallinn, 2018 (in Estonian).
https://riigihanked.riik.ee/rhr-web/#/procurement/722774/general-info (accessed 20.07.2020).
12. Koitmets, K., Reinsalu, E., Valgma, I. Precision of oil shale energy rating and oil shale resources. Oil Shale, 2003, 20(1), 15−24.
13. Toomik, P. Geological conditions and technological possibilities of oil shale mining in Narva opencast. MSc Thesis, Tallinn University of Technology, Tallinn, 2015 (in Estonian).
https://digikogu.taltech.ee/et/Item/ebfb226d-3610-400b-a762-21a6c05fb80b (accessed 06.12.2020).
14. Credo Dialogue. 2014-2020.
https://credo-dialogue.ru/ (accessed 06.12.2020).
15. Specified requirements for mining surveys. RT I, 18.08.2020, 4 (in Estonian).
https://www.riigiteataja.ee/akt/107052019005 (accessed 21.08.2020).
16. Zlot, R., Bosse, M. Efficient large-scale three-dimensional mobile mapping for underground mines. J. Field Robot., 2014, 31(5), 758−779.
https://doi.org/10.1002/rob.21504
17. ZEB-REVO User Manual v3.0.0 GeoSLAM Ltd, 2017.
http://download.geoslam.com/docs/zeb-revo/ZEB-REVO%20User%20Guide%20V3.0.0.pdf (acces-sed 20.07.2020).
18. Faro Product Info. Faro Laser Scanner Focus3D X330 HDR. Tech Sheet, 2016.
https://faro.app.box.com/s/kaeo5jwguxe1tacvzea7berpzm0phofp/file/441644006961 (accessed 20.07.2020).
19. Malberg, K. Determining the Shape and Volume of Enclosed Spaces and Underground Mines by Mobile Laser Scanning. MSc Thesis, Tallinn University of Technology, Tallinn, 2019 (in Estonian).
https://digi.lib.ttu.ee/i/?12484& (accessed 29.07.2019).
20. Durrant-Whyte, H., Bailey, T. Simultaneous localization and mapping (SLAM): part I. The essential algorithms. IEEE Robot. Autom. Mag., 2006, 13(2), 99−110.
https://doi.org/10.1109/MRA.2006.1638022
21. 3DReshaper. Technodigit part of Hexagon, 2020.
https://www.3dreshaper.com/en/software-en/ (accessed 06.12.2020).
22. The Earth’s Crust Act, RT I, 21.12.2019, 11. (In Estonian).
https://www.riigi-teataja.ee/ akt/MaaPS (accessed 06.12.2020).
