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Eksperymentalne badanie możliwości zastosowania UAV typu DJI Phantom 4 RTK w trybie PPK do tworzenia 3D modeli w kopalniach odkrywkowych
Języki publikacji
Abstrakty
Open-pit coal mines’ terrain is often complex and quickly and frequently changes. Therefore, topographic surveys of open-pit mines are undertaken on a daily basis. While these tasks are very time-consuming and costly with traditional methods such as total station and GNSS, the unmanned aerial vehicle (UAV) based method can be more efficient. This method is a combination of the “Structure from motion” (SfM) photogrammetry technique and UAV photogrammetry which has been widely used in topographic surveying. With an increasing popularity of RTK-enabled drones, it is becoming even more powerful method. While the important role of ground control points (GCP) in the accuracy of digital surface model (DSM) generated from images acquired by “traditional” UAVs (not RTK-enabled drones) has been proved in many previous studies, it is not clear in the case of RTK-enabled drones, especially for complex terrain in open-pit coal mines. In this study, we experimentally investigated the influence of GCP regarding its numbers and distribution on the accuracy of DSM generation from images acquired by RTK-enabled drones in open-pit coal mines. In addition, the Post Processing Kinematic (PPK) mode was executed over a test field with the same flight altitude. DSM generation was performed with several block control configurations: PPK only, PPK with one GCP, and PPK with two GCPs. Several positions of GCPs were also examined to test the optimal locations for placing GCPs to achieve accurate DSMs. The results show that the horizontal and vertical accuracy given by PPK only were 9.3 and 84.4 cm, respectively. However, when adding at least one GCP, the accuracy was significantly improved in both horizontal and vertical components, with RMSE for XY and Z ranging between 3.8 and 9.8 cm (with one GCP) and between 3.0 and 5.7 cm (with two GCPs), respectively. Also, the GCPs placed in the deep areas of the open-pit mine could ensure the cm-level accuracy.
Tereny kopalni odkrywkowych w Wietnamie są często pozbawione roślinności o silnie zróżnicowanej morfologii utworzone w wyniku eksploatacji górniczej. Tradycyjne prace geodezyjne w kopalniach odkrywkowych są czasochłonne. W artykule, przedstawiono wyniki badania dotyczącego procesu technologicznego generowania 3D modeli i ortofotomapy na podstawie danych pozyskanych z pokładu bezzałogowej platformy UAV typu DJI Phantom 4RTK. Współcześnie bezzałogowe statki powierzchne (BSP) stanowią̨ dobrze rozwiniętą gałąź́ lotnictwa, która umożliwia pozyskiwanie danych z pułapu od kilku do kilkuset metrów. Własność́ ta stwarza nowe możliwości zastosowanie UAV w w kopalniach odkrywkowych. Omawiano metodę połączenia techniki fotogrametrii „Struktury z ruchu” (SfM) i fotogrametrii UAV, która jest szeroko stosowana w pomiarach topograficznych. Podczas gdy ważna rola naziemnych punktów kontrolnych (GCP) w dokładności cyfrowego modelu powierzchni (DSM) generowanego na podstawie obrazów uzyskanych przez „tradycyjne” UAV (a nie drony z obsługą RTK) została udowodniona w wielu poprzednich badaniach, nie jest to jasne w przypadek dronów obsługujących RTK, zwłaszcza na skomplikowanym terenie w odkrywkowych kopalniach węgla. W tym badaniu eksperymentalnie zbadano wpływ GCP pod względem jego liczby i rozmieszczenia na dokładność generowania DSM na podstawie obrazów uzyskanych przez drony z obsługą RTK w odkrywkowych kopalniach węgla. Dodatkowo, tryb Post Processing Kinematic (PPK) został uruchomiony na polu testowym na tej samej wysokości lotu. Generowanie DSM przeprowadzono z kilkoma konfiguracjami sterowania blokami: tylko PPK, PPK z jednym GCP i PPK z dwoma GCP. Zbadano również kilka pozycji GCP, aby przetestować optymalne lokalizacje do umieszczania GCP w celu uzyskania dokładnych DSM. Wyniki pokazują, że podana przez PPK dokładność pozioma i pionowa wyniosła odpowiednio 9,3 i 84,4 cm. Jednak po dodaniu co najmniej jednego GCP dokładność została znacznie poprawiona zarówno w komponentach poziomych, jak i pionowych, przy RMSE dla XY i Z w zakresie od 3,8 do 9,8 cm (z jednym GCP) i od 3,0 do 5,7 cm (z dwoma GCP), odpowiednio. Ponadto GCP umieszczone w głębokich obszarach kopalni odkrywkowej mogą zapewnić dokładność w granicach centymetrowych (cm).
Czasopismo
Rocznik
Tom
Strony
65--74
Opis fizyczny
Bibliogr. 30 poz., tab., wykr., zdj.
Twórcy
autor
- Department of Mine Surveying, Hanoi University of Mining and Geology, Hanoi; Vietnam
autor
- Department of Mine Surveying, Hanoi University of Mining and Geology, Hanoi; Vietnam
autor
- Department of Mine Surveying, Hanoi University of Mining and Geology, Hanoi; Vietnam
autor
- Department of Mine Surveying, Hanoi University of Mining and Geology, Hanoi; Vietnam
autor
- Department of Photogrammetry and Remote Sensing, Hanoi University of Mining and Geology, Hanoi, Vietnam
autor
- Department of Surface Mining, Hanoi University of Mining and Geology, Hanoi, Vietnam
Bibliografia
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- 4. Dieu Tien Bui, Nguyen Quoc Long, Bui Xuan Nam, Nguyen Viet Nghia, Pham Van Chung, Le Van Canh, . . . Bjørn Kristoffersen. (2017). Lightweight Unmanned Aerial Vehicle and Structure-from-Motion Photogrammetry for Generating Digital Surface Model for Open-Pit Coal Mine Area and Its Accuracy Assessment. International Conference on Geo-Spatial Technologies and Earth Resources, 17-33.
- 5. Dinkov, D. (2019). A Low Cost Method UAV-PPK -Accuracy and Application.
- 6. Dinkov, D., & Kitev, A. (2020). Advantages, disadvantages and applicability of GNSS post-processing kinematic (PPK) method for direct georeferencing of UAV images.
- 7. Ekaso, D., Nex, F., & Kerle, N. (2020). Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing. Geo-spatial Information Science, 1-17. doi:10.1080/10095020.2019.1710437
- 8. Fazeli, H., Samadzadegan, F., & Dadrass Javan, F. (2016). Evaluating the potential of RTK-UAV for automatic point cloud generation in 3D rapid mapping. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B6, 221-226. doi:10.5194/isprsarchives-XLI-B6-221-2016
- 9. Forlani, G., Dall’Asta, E., Diotri, F., Cella, U. M. d., Roncella, R., & Santise, M. (2018). Quality assessment of DSMs produced from UAV flights georeferenced with on-board RTK positioning. Remote Sensing, 10(2), 311.
- 10. Gianfranco, F., Elisa, D. a., Fabrizio, D., Umberto Morra Di, C., Riccardo, R., & Marina, S. (2018). Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning. Remote Sensing, 10(2), 311. doi:10.3390/rs10020311
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- 13. Hugenholtz, C., Brown, O., Walker, J., Barchyn, T., Nesbit, P., Kucharczyk, M., & Myshak, S. (2016). Spatial Accuracy of UAV-Derived Orthoimagery and Topography: Comparing Photogrammetric Models Processed with Direct Geo-Referencing and Ground Control Points. GEOMATICA, 70(1), 21-30. doi:10.5623/cig2016-102
- 14. Hugenholtz, C. H., Walker, J., Brown, O., & Myshak, S. (2015). Earthwork Volumetrics with an Unmanned Aerial Vehicle and Softcopy Photogrammetry. Journal of Surveying Engineering, 141(1).
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- 16. Le Van Canh, Cao Xuan Cuong, Le Hong Viet, & Dinh Tien. (2020). Volume computation of quarries in Vietnam based on Unmanned Aerial Vehicle (UAV) data. Mining and earth sciences, 61(1), 21-30. doi:10.46326/JMES.2020.61(1).03
- 17. Lee, S., & Choi, Y. (2015). Topographic survey at small-scale open-pit mines using a popular rotary-wing unmanned aerial vehicle (drone). Tunnel & Underground Space, 25, 462–469.
- 18. Mian, O., Lutes, J., Lipa, G., Hutton, J., Gavelle, E., & Borghini, S. (2015). Direct georeferencing on small Unmanned Aerial platforms for improved reliability and accuracy of mapping without the need for ground control points. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, xl(1), 397-402. doi:10.5194/isprsarchives-XL-1-W4-397-2015
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- 21. Nguyen Viet Nghia, Nguyen Quoc Long, Nguyen Thi Cuc, & Bui Xuan Nam. (2019). Applied Terrestrial Laser Scanning for coal mine high definition mapping. World of Mining - Surface and Underground, 4(1613-2408), 237-242.
- 22. Siebert, S., & Teizer, J. (2014). Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Automation in Construction, 41, 1-14. doi:10.1016/j.autcon.2014.01.004
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- 26. Thiel, c., mueller, m., berger, c., cremer, f., dubois, c., hese, S., . . . Pathe, C. (2020). Monitoring Selective Logging in a Pine-Dominated Forest in Central Germany with Repeated Drone Flights Utilizing A Low Cost RTK Quadcopter. Drones, 4. doi:10.3390/drones4020011
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- 29. Xuan-Nam, B., Nguyen, H., Hai-An, L., Hoang-Bac, B., & Ngoc-Hoan, D. (2020). Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques. Natural Resources Research, 29(2), 571-591. doi:10.1007/s11053-019-09461-0
- 30. Zhang, H., Aldana Jague, E., Clapuyt, F., Wilken, F., Vanacker, V., & Oost, K. (2019). Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection. Earth Surface Dynamics, 7. doi:10.5194/esurf-7-807-2019
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-30308b00-1772-4480-a3ac-374bb0e48db0