Experimental Investigation on the Performance of DJI Phantom 4 RTK in the PPK Mode for 3D Mapping Open-Pit Mines

Abstract

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.

Photogrammetry Using UAV-Mounted GNSS RTK: Georeferencing Strategies without GCPs

“…This elevation error is random in a way, as it differed even in duplicate flights with the same configuration and the same UAV. This is in accordance with the findings by other authors (e.g., [34,36,38,39]), regardless of whether a fixed-wing or rotary-wing UAV was used.…”

“…A detailed analysis of the results revealed that incorrect calculation of the internal orientation elements, particularly of the focal length f, is the most likely reason for this problem (e.g., [34,38,40]). Various strategies to deal with this problem have been proposed, such as the use of oblique images (e.g., [19,36,40]), including a small number of GCPs (e.g., [36,39,40,44]) or camera pre-calibration (e.g., [34,42]). Disregarding the use of GCPs (a reliable solution which, however, negates the principal goal of this study, i.e., the simplification of the measurement by avoiding the use of GCPs), it is necessary to try to propose a measurement strategy (flight configuration, processing method, other techniques) to prevent the systematic error.…”

“…Additionally, the systematic error was shown to be more or less random as it differed even between repeated flights on the same site with the same conditions (the same UAV and processed in the same way) [34]. A small number of GCPs [39], or even a single one [34], was, however, shown to significantly reduce this problem. Several studies also suggested methods that should suppress or even remove this phenomenon.…”

Multi-View and Shift Rasterization Algorithm (MVSR) for Effective Identification of Ground in Dense Point Clouds

“…Such approaches to capturing the reality are being employed in many fields, from reality modeling for identification of historical monuments [6], infrastructure mapping [7], mapping of open-pit mines [8], monitoring of changes during mining [9], coastline mapping [10], intertidal reef analysis [11], measurements for the purposes of building reconstruction [12], earthworks volume calculations [13], archaeological documentation [14], etc. Often, the data are processed to produce a general [15] or specialized [16] Digital Terrain Model (DTM).…”

Multi-View and Shift Rasterization Algorithm (MVSR) for Effective Identification of Ground in Dense Point Clouds

“…Such approaches to capturing the reality are being employed in many fields, from reality modeling for the identification of historical monuments [6], infrastructure mapping [7], mapping of open-pit mines [8], monitoring of changes during mining [9], coastline mapping [10], intertidal reef analysis [11], measurements for the purposes of building reconstruction [12], earthworks volume calculations [13], archaeological documentation [14], etc. Often, the data are processed to produce a general [15] or specialized [16] digital terrain model (DTM).…”