High-Precision UAV Photogrammetry with RTK GNSS: Eliminating Ground Control Points

Abstract

The advancements in Unmanned Aerial Vehicles (UAVs) have significantly enhanced the capability of the photogrammetric approaches, particularly with the integration of Real-Time Kinematic (RTK) sensors. That approach enables the operators to use the Global Navigation Satellite System (GNSS) more efficiently with the production of high-precision 3D Digital Terrain Models (DTMs). Traditionally, Ground Control Points (GCPs) are used to link those models to a ground coordinate system, but their establishment is time-consuming and labor-intensive, requiring static or rapid-static GNSS observations over two hours for each point. However, RTK-embedded UAVs offer a significant improvement by facilitating direct geo-referencing of DTMs, which includes the estimation of internal and external orientation parameters more efficiently and potentially eliminating the need for GCPs. In this study, UAV flights over a test area at various altitudes (30m, 45m, 60m) were conducted to evaluate the 3D positioning accuracy of photogrammetric models generated without using any GCP, and their locations were compared against the precise GNSS observations for 22 control points. Results indicated that UAVs with RTK ability could achieve centimeter-level accuracy in positioning, making this kind of evaluation a viable alternative to traditional methods. This study also discusses the implications of those results within the context of large-scale map production and their regulations in Türkiye. The elimination of GCPs should significantly reduce the time and effort associated with map production, suggesting a potential alternative in regulatory standards to incorporate these technological approaches.

Keywords

• UAV
• RTK GNSS
• photogrammetry
• direct geo-referencing
• 3D modeling
• GCP
• Türkiye mapping regulations

References

1. Pu S, Vosselman G. Knowledge-based reconstruction of building models from terrestrial laser scanning data. ISPRS J Photogramm Remote Sens. 2009;64(6):575–84. doi: 10.1016/j.isprsjprs.2009.04.001.
2. Leite F, Akcamete A, Akinci B, Atasoy G, Kiziltas S. Analysis of modeling effort and impact of different levels of detail in building information models. Autom Constr. 2011;20:601–9. doi: 10.1016/j.autcon.2010.11.027.
3. Quagliarini E, Clini P, Ripanti M. Fast, low-cost and safe methodology for the assessment of the state of conservation of historical buildings from 3D laser scanning: The case study of Santa Maria in Portonovo (Italy). J Cult Herit. 2017;24:175–83. doi: 10.1016/j.culher.2016.10.006.
4. Taddia Y, González-García L, Zambello E, Pellegrinelli A. Quality assessment of photogrammetric models for façade and building reconstruction using DJI Phantom 4 RTK. Remote Sens. 2020;12(19):3144. doi: 10.3390/rs12193144.
5. Day D, Weaver W, Wilsing L. Accuracy of UAS photogrammetry: A comparative evaluation. Photogramm Eng Remote Sensing. 2016;82(12):909-14. doi: 10.14358/PERS.82.12.909.
6. Sanz-Ablanedo E, Chandler JH, Rodríguez-Pérez JR, Ordóñez C. Accuracy of unmanned aerial vehicle (UAV) and SfM photogrammetry survey as a function of the number and location of ground control points used. Remote Sens. 2018;10(10):1606. doi: 10.3390/rs10101606.
7. Elkhrachy I. Accuracy assessment of low-cost unmanned aerial vehicle (UAV) photogrammetry. Alexandria Eng J. 2021;60:5579–90. doi: 10.1016/j.aej.2021.04.011.
8. Ansari A. Use of point cloud with a low-cost UAV system for 3D mapping. In: Proceedings of the 2012 International Conference on Emerging Trends in Electrical Engineering and Energy Management (ICETEEEM); 2012 Dec 13-15; Chennai, India. IEEE; 2012. p. 131-134. doi: 10.1109/ICETEEEM.2012.6494471.

9. Daponte P, De Vito L, Mazzilli G, Picariello F, Rapuano S. A height measurement uncertainty model for archaeological surveys by aerial photogrammetry. Measurement. 2017;98:192-8. doi: 10.1016/j.measurement.2016.11.033.
10. Sun S, Wang B. Low-altitude UAV 3D modeling technology in the application of ancient buildings protection situation assessment. Energy Procedia. 2018;153:320–4. doi: 10.1016/j.egypro.2018.10.082.
11. Vitale V. The case of the middle valley of the Sinni (Southern Basilicata). Methods of archaeological and architectural documentation: 3D photomodelling techniques and use of RPAS. Digit Appl Archaeol Cult Herit. 2018;11:e00084. doi: 10.1016/j.daach.2018.e00084.
12. Zheng X, Wang F, Li Z. A multi-UAV cooperative route planning methodology for 3D fine-resolution building model reconstruction. ISPRS J Photogramm Remote Sens. 2018;146:483–94. doi: 10.1016/j.isprsjprs.2018.11.004.
13. Mavroulis S, Andreadakis E, Spyrou NI, Antoniou V, Skourtsos E, Papadimitriou P, et al. UAV and GIS-based rapid earthquake-induced building damage assessment and methodology for EMS-98 isoseismal map drawing: The June 12, 2017 Mw 6.3 Lesvos earthquake. Int J Disaster Risk Reduct. 2019;37:101169. doi: 10.1016/j.ijdrr.2019.101169.
14. Jones CA, Church E. Photogrammetry is for everyone: Structure-from-motion software user experiences in archaeology. J Archaeol Sci Rep. 2020;30:102261. doi: 10.1016/j.jasrep.2020.102261.
15. Hill AC, Laugier EJ, Casana J. Archaeological remote sensing using multi-temporal, drone-acquired thermal and near infrared (NIR) imagery: A case study at the Enfield Shaker Village, New Hampshire. Remote Sens. 2020;12(4):690. doi: 10.3390/rs12040690.
16. Ekaso D, Nex F, Kerle N. Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing. Geo-Spatial Inf Sci. 2020;23(2):165–81. doi: 10.1080/10095020.2019.1710437.
17. Eltner A, Sofia G. Structure from motion photogrammetric technique. Developments in Earth Surface Processes. Amsterdam: Elsevier; 2020. Vol. 23, p. 1–24. doi: 10.1016/B978-0-444-64177-9.00001-1.
18. Stott E, Williams RD, Hoey TB. Ground control point distribution for accurate kilometre-scale topographic mapping using an RTK-GNSS unmanned aerial vehicle and SfM photogrammetry. Drones. 2020;4(3):55. doi: 10.3390/drones4030055.
19. Benassi F, Dall’Asta E, Diotri F, Forlani G, di Cella UM, Roncella R, et al. Testing accuracy and repeatability of UAV blocks oriented with GNSS-supported aerial triangulation. Remote Sens. 2017;9(2):172. doi: 10.3390/rs9020172.
20. Varbla S, Pusst R, Ellman A. Accuracy assessment of RTK-GNSS equipped UAV conducted as-built surveys for construction site modelling. Surv Rev. 2020;53(381):477–92. doi: 10.1080/00396265.2020.1830544.
21. TMMOB Harita ve Kadastro Mühendisleri Odası. Büyük Ölçekli Harita ve Harita Bilgileri Üretim Yönetmeliği [Internet]. Ankara: TMMOB Harita ve Kadastro Mühendisleri Odası; 2018. [cited 2024 Nov 25] Available from: https://www.harita.gov.tr/uploads/files/regulations/buyuk-olcekli-harita-ve-harita-bilgileri-uretim- yonetmeligi-65.pdf
22. Pix4D. Pix4D Documentation [Internet]. [cited 2024 Nov 25] Available from: https://support.pix4d.com/hc/en- us/articles/202557489
23. TUSAGA-AKTİF. Türkiye Ulusal Sabit GNSS Ağı-Aktif. [cited 2024 Nov 25] Available from: https://www.tusaga- aktif.gov.tr/
24. Harwin S, Lucieer A. Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sens. 2012;4(6):1573–99. doi: 10.3390/rs4061573.
25. Štroner M, Urban R, Reindl T, Seidl J, Broucek J. Evaluation of the georeferencing accuracy of a photogrammetric model using a quadrocopter with onboard GNSS RTK. Sensors. 2020;20(8):2318. doi: 10.3390/s20082318.
26. Taddia Y, Stecchi F, Pellegrinelli A. Using DJI Phantom 4 RTK drone for topographic mapping of coastal areas. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci. 2019;XLII-2/W13:625–30. doi: 10.5194/isprs-archives-XLII-2- W13-625-2019.
27. James MR, Robson S, d’Oleire-Oltmanns S, Niethammer U. Optimising UAV topographic surveys processed with structure-from-motion: Ground control quality, quantity and bundle adjustment. Geomorphology. 2017;280:51– 66. doi: 10.1016/j.geomorph.2016.11.021.
28. Woodget AS, Carbonneau PE, Visser F, Maddock IP. Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth Surf Process Landf. 2015;40:47–64. doi: 10.1002/esp.3613.
29. Woodget AS, Austrums R, Maddock IP, Habit E. Drones and digital photogrammetry: From classifications to continuums for monitoring river habitat and hydromorphology. WIREs Water. 2017;4:e1222. doi: 10.1002/wat2.1222.
30. Martinez-Carricondo P, Agüera-Vega F, Carvajal-Ramirez F, Mesas-Carrascosa F-J, Garcia-Ferrer A, Perez- Porras F-J. Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points. Int J Appl Earth Obs Geoinf. 2018;72:1–10. doi: 10.1016/j.jag.2018.05.015.
31. Kahveci M. Kinematik GNSS ve RTK CORS Ağları. Ankara: Nobel; 2017.
32. DJI Enterprise. DJI Mavic 3 Enterprise Specs. [cited 2024 Nov 25] Available from: https://enterprise.dji.com/mavic-3-enterprise/specs
33. Enterprise. DJI Terra. [cited 2024 Nov 25] Available from: https://enterprise.dji.com/dji-terra
34. Gehan EA. A generalized Wilcoxon test for comparing arbitrarily singly-censored samples. Biometrika. 1965;52(1-2):203–24. doi: 10.1093/biomet/52.1-2.203.
35. Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika. 1965;52(3- 4):591–611. doi: 10.2307/2333709.
36. Hedberg EC, Ayers S. The power of a paired t-test with a covariate. Soc Sci Res. 2015;50:277–91. doi: 10.1016/j.ssresearch.2014.12.004.