Assessment of GCP Number and Separation Distance for Small UAS Surveys with and without GNSS-PPK Positioning

Bolkas, Dimitrios

doi:10.1061/(asce)su.1943-5428.0000283

Cited by 48 publications

(43 citation statements)

References 31 publications

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“…In order to testing the potential of direct georeferencing with DJI-P4RTK with as few GCPs as possible, projects were created varying the number and the position of GCPs [27][28][29]. In particular, as already pointed out by Taddia et al using RTK camera locations for the same datasets [22] as well as Forlani et al [30] with a fixed wing aircraft, the nadiral dataset necessarily requires the use of at least one GCP for the model to obtain good accuracy.…”

Section: Generation Of Photogrammetric Models and Dtmsmentioning

confidence: 99%

Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode

Taddia

Stecchi²,

Pellegrinelli

2020

Drones

125

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Topographic and geomorphological surveys of coastal areas usually require the aerial mapping of long and narrow sections of littoral. The georeferencing of photogrammetric models is generally based on the signalization and survey of Ground Control Points (GCPs), which are very time-consuming tasks. Direct georeferencing with high camera location accuracy due to on-board multi-frequency GNSS receivers can limit the need for GCPs. Recently, DJI has made available the Phantom 4 Real-Time Kinematic (RTK) (DJI-P4RTK), which combines the versatility and the ease of use of previous DJI Phantom models with the advantages of a multi-frequency on-board GNSS receiver. In this paper, we investigated the accuracy of both photogrammetric models and Digital Terrain Models (DTMs) generated in Agisoft Metashape from two different image datasets (nadiral and oblique) acquired by a DJI-P4RTK. Camera locations were computed with the Post-Processing Kinematic (PPK) of the Receiver Independent Exchange Format (RINEX) file recorded by the aircraft during flight missions. A Continuously Operating Reference Station (CORS) located at a 15 km distance from the site was used for this task. The results highlighted that the oblique dataset produced very similar results, with GCPs (3D RMSE = 0.025 m) and without (3D RMSE = 0.028 m), while the nadiral dataset was affected more by the position and number of the GCPs (3D RMSE from 0.034 to 0.075 m). The introduction of a few oblique images into the nadiral dataset without any GCP improved the vertical accuracy of the model (Up RMSE from 0.052 to 0.025 m) and can represent a solution to speed up the image acquisition of nadiral datasets for PPK with the DJI-P4RTK and no GCPs. Moreover, the results of this research are compared to those obtained in RTK mode for the same datasets. The novelty of this research is the combination of a multitude of aspects regarding the DJI Phantom 4 RTK aircraft and the subsequent data processing strategies for assessing the quality of photogrammetric models, DTMs, and cross-section profiles.

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Section: Generation Of Photogrammetric Models and Dtmsmentioning

confidence: 99%

Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode

Taddia

Stecchi²,

Pellegrinelli

2020

Drones

125

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“…An area of interest in current research involves looking at ways to reduce the errors of surface models through image acquisition techniques and by using different photogrammetric algorithms [32,51]. Technique-based approaches include adjusting flying height, flight pattern, overlap, camera tilt, GCP distribution, and using different photogrammetric software.…”

Section: Discussionmentioning

confidence: 99%

“…Ideally, the camera calibration parameters should be estimated in laboratory settings, however, these parameters often change under in-flight conditions [48], therefore, most practitioners prefer to apply the self-calibration method on a flight-to-flight basis [49,50]. Depending on several factors, such as flight configuration (e.g., flying height, overlap, and image orientation), environmental conditions, surface complexity, purpose of sUAS survey, number and distribution of GCPs, quality of GCPs and the flying and shutter speed, the camera self-calibration parameters can vary [51]. Having a low number of GCPs can lead to a poor self-calibration and may lower the accuracy of the sUAS-derived point cloud, DSM, and orthophoto [51].…”

Section: Camera Self-calibrationmentioning

confidence: 99%

“…Depending on several factors, such as flight configuration (e.g., flying height, overlap, and image orientation), environmental conditions, surface complexity, purpose of sUAS survey, number and distribution of GCPs, quality of GCPs and the flying and shutter speed, the camera self-calibration parameters can vary [51]. Having a low number of GCPs can lead to a poor self-calibration and may lower the accuracy of the sUAS-derived point cloud, DSM, and orthophoto [51]. Therefore, it is critical to test several self-calibration scenarios to optimize the self-calibration parameters for each sUAS survey.…”

Section: Camera Self-calibrationmentioning

confidence: 99%

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Comparing sUAS Photogrammetrically-Derived Point Clouds with GNSS Measurements and Terrestrial Laser Scanning for Topographic Mapping

Mora

Suleiman

Chen

et al. 2019

Drones

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Interest in small unmanned aircraft systems (sUAS) for topographic mapping has significantly grown in recent years, driven in part by technological advancements that have made it possible to survey small- to medium-sized areas quickly and at low cost using sUAS aerial photography and digital photogrammetry. Although this approach can produce dense point clouds of topographic measurements, they have not been tested extensively to provide insights on accuracy levels for topographic mapping. This case study examines the accuracy of a sUAS-derived point cloud of a parking lot located at the Citizens Bank Arena (CBA) in Ontario, California, by comparing it to ground control points (GCPs) measured using global navigation satellite system (GNSS) data corrected with real-time kinematic (RTK) and to data from a terrestrial laser scanning (TLS) survey. We intentionally chose a flat surface due to the prevalence of flat scenes in sUAS mapping and the challenges they pose for accurately deriving vertical measurements. When the GNSS-RTK survey was compared to the sUAS point cloud, the residuals were found to be on average 18 mm and −20 mm for the horizontal and vertical components. Furthermore, when the sUAS point cloud was compared to the TLS point cloud, the average difference observed in the vertical component was 2 mm with a standard deviation of 31 mm. These results indicate that sUAS imagery can produce point clouds comparable to traditional topographic mapping methods and support other studies showing that sUAS photogrammetry provides a cost-effective, safe, efficient, and accurate solution for topographic mapping.

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“…Accordingly, in the last years, several studies have focused on the use of UAVs to generate both 3D models and added-value metric products by means of SfM approaches. These have addressed different aspects (e.g., the optimization of flight planning and image acquisition phases [34,35], the use of oblique images [36], the optimization of the control points configuration [37][38][39], the assessment of geometric accuracy of photogrammetric products [40], and the camera calibration [41,42]).…”

Section: Unmanned Aerial Vehicles (Uavs) and Structure From Motion (Smentioning

confidence: 99%

Boosting the Timeliness of UAV Large Scale Mapping. Direct Georeferencing Approaches: Operational Strategies and Best Practices

Losè

Chiabrando

Tonolo

2020

IJGI

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The use of unmanned aerial vehicles (UAVs) is nowadays a standard approach in several application fields. Researches connected with these systems cover several topics and the evolution of these platforms and their applications are rapidly growing. Despite the high level of automatization reached nowadays, there is still a phase of the overall UAVs’ photogrammetric pipeline that requires a high effort in terms of time and resources (i.e., the georeferencing phase). However, thanks to the availability of survey-grade GNSS (Global Navigation Satellite System) receivers embedded in the aerial platforms, it is possible to also enhance this phase of the processing by adopting direct georeferencing approaches (i.e., without using any ground control point and exploiting real time kinematic (RTK) positioning). This work investigates the possibilities offered by a multirotor commercial system equipped with a RTK-enabled GNSS receiver, focusing on the accuracy of the georeferencing phase. Several tests were performed in an ad-hoc case study exploiting different georeferencing solutions and assessing the 3D positional accuracies, thanks to a network of control points. The best approaches to be adopted in the field according to accuracy requirements of the final map products were identified and operational guidelines proposed accordingly.

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Assessment of GCP Number and Separation Distance for Small UAS Surveys with and without GNSS-PPK Positioning

Cited by 48 publications

References 31 publications

Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode

Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode

Comparing sUAS Photogrammetrically-Derived Point Clouds with GNSS Measurements and Terrestrial Laser Scanning for Topographic Mapping

Boosting the Timeliness of UAV Large Scale Mapping. Direct Georeferencing Approaches: Operational Strategies and Best Practices

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