Detecting Geo-Positional Bias in Imagery Collected Using Small UASs

2021 ◽  
Vol 87 (9) ◽  
pp. 631-638
Author(s):  
Jonathan B. Thayn ◽  
Aaron M. Paque ◽  
Megan C. Maher
Keyword(s):  
Global Positioning System ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
Positional Accuracy ◽  
Directional Bias ◽  
Unequal Variances ◽  
Ground Control Points ◽  
Positional Bias ◽  
Global Positioning ◽  
Aerial Systems

Statistical methods for detecting bias in global positioning system (<small>GPS</small>) error are presented and applied to imagery collected using three common unmanned aerial systems (<small>UASs</small>). Imagery processed without ground control points (<small>GCPs</small>) had horizontal errors of 1.0–2.5 m; however, the errors had unequal variances, significant directional bias, and did not conform to the expected statistical distribution and so should be considered unreliable. When <small>GCPs</small>were used, horizontal errors decreased to less than 5 cm, and the errors had equal variances, directional uniformity, and they conformed to the expected distribution. The analysis identified a longitudinal bias in some of the reference data, which were subsequently excluded from the analysis. Had these data been retained, the estimates of positional accuracy would have been unreliable and inaccurate. These results strongly suggest that examining <small>GPS</small> data for bias should be a much more common practice.

scholarly journals Accuracy of 3D Landscape Reconstruction without Ground Control Points Using Different UAS Platforms

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 13 ◽  
Author(s):  
Margaret Kalacska ◽  
Oliver Lucanus ◽  
J. Pablo Arroyo-Mora ◽  
Étienne Laliberté ◽  
Kathryn Elmer ◽  
...  
Keyword(s):  
Low Cost ◽  
Ground Control ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
Model Errors ◽  
Positional Accuracy ◽  
Ground Control Points ◽  
Digital Elevation ◽  
High Level ◽  
Aerial Systems

The rapid increase of low-cost consumer-grade to enterprise-level unmanned aerial systems (UASs) has resulted in the exponential use of these systems in many applications. Structure from motion with multiview stereo (SfM-MVS) photogrammetry is now the baseline for the development of orthoimages and 3D surfaces (e.g., digital elevation models). The horizontal and vertical positional accuracies (x, y and z) of these products in general, rely heavily on the use of ground control points (GCPs). However, for many applications, the use of GCPs is not possible. Here we tested 14 UASs to assess the positional and within-model accuracy of SfM-MVS reconstructions of low-relief landscapes without GCPs ranging from consumer to enterprise-grade vertical takeoff and landing (VTOL) platforms. We found that high positional accuracy is not necessarily related to the platform cost or grade, rather the most important aspect is the use of post-processing kinetic (PPK) or real-time kinetic (RTK) solutions for geotagging the photographs. SfM-MVS products generated from UAS with onboard geotagging, regardless of grade, results in greater positional accuracies and lower within-model errors. We conclude that where repeatability and adherence to a high level of accuracy are needed, only RTK and PPK systems should be used without GCPs.

scholarly journals Assessing the Accuracy of Digital Surface Models Derived from Optical Imagery Acquired with Unmanned Aerial Systems

Drones ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 15 ◽  
Author(s):  
Salvatore Manfreda ◽  
Petr Dvorak ◽  
Jana Mullerova ◽  
Sorin Herban ◽  
Pietro Vuono ◽  
...  
Keyword(s):  
3D Model ◽  
Low Cost ◽  
Three Dimensional ◽  
Careful Consideration ◽  
Ground Control ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
Earthen Dam ◽  
Ground Control Points ◽  
Aerial Systems

Small unmanned aerial systems (UASs) equipped with an optical camera are a cost-effective strategy for topographic surveys. These low-cost UASs can provide useful information for three-dimensional (3D) reconstruction even if they are equipped with a low-quality navigation system. To ensure the production of high-quality topographic models, careful consideration of the flight mode and proper distribution of ground control points are required. To this end, a commercial UAS was adopted to monitor a small earthen dam using different combinations of flight configurations and by adopting a variable number of ground control points (GCPs). The results highlight that optimization of both the choice and combination of flight plans can reduce the relative error of the 3D model to within two meters without the need to include GCPs. However, the use of GCPs greatly improved the quality of the topographic survey, reducing error to the order of a few centimeters. The combined use of images extracted from two flights, one with a camera mounted at nadir and the second with a 20° angle, was found to be beneficial for increasing the overall accuracy of the 3D model and especially the vertical precision.

scholarly journals HEIGHT ACCURACY BASED ON DIFFERENT RTK GPS METHOD FOR ULTRALIGHT AIRCRAFT IMAGES

2015 ◽  
Vol XL-3/W3 ◽  
pp. 287-292
Author(s):  
K. N. Tahar
Keyword(s):  
Global Positioning System ◽  
Local Network ◽  
Ground Control ◽  
Positioning System ◽  
Control Points ◽  
Ground Control Points ◽  
Global Positioning ◽  
Time Correction ◽  
Rtk Gps ◽  
Gps Station

Height accuracy is one of the important elements in surveying work especially for control point’s establishment which requires an accurate measurement. There are many methods can be used to acquire height value such as tacheometry, leveling and Global Positioning System (GPS). This study has investigated the effect on height accuracy based on different observations which are single based and network based GPS methods. The GPS network is acquired from the local network namely Iskandar network. This network has been setup to provide real-time correction data to rover GPS station while the single network is based on the known GPS station. Nine ground control points were established evenly at the study area. Each ground control points were observed about two and ten minutes. It was found that, the height accuracy give the different result for each observation.

scholarly journals Using Digital Surface Models from UAS Imagery of Fire Damaged Sphagnum Peatlands for Monitoring and Hydrological Restoration

Drones ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 45 ◽  
Author(s):  
Shannon de Roos ◽  
Darren Turner ◽  
Arko Lucieer ◽  
David M.J.S. Bowman
Keyword(s):  
Unmanned Aerial Systems ◽  
Control Points ◽  
Hydrological Models ◽  
Slow Recovery ◽  
Ground Control Points ◽  
Surface Models ◽  
Good Potential ◽  
Northern Peatlands ◽  
Aerial Systems

The sub-alpine and alpine Sphagnum peatlands in Australia are geographically constrained to poorly drained areas c. 1000 m a.s.l. Sphagnum is an important contributor to the resilience of peatlands; however, it is also very sensitive to fire and often shows slow recovery after being damaged. Recovery is largely dependent on a sufficient water supply and impeded drainage. Monitoring the fragmented areas of Australia’s peatlands can be achieved by capturing ultra-high spatial resolution imagery from an unmanned aerial systems (UAS). High resolution digital surface models (DSMs) can be created from UAS imagery, from which hydrological models can be derived to monitor hydrological changes and assist with rehabilitation of damaged peatlands. One of the constraints of the use of UAS is the intensive fieldwork required. The need to distribute ground control points (GCPs) adds to fieldwork complexity. GCPs are often used for georeferencing of the UAS imagery, as well as for removal of artificial tilting and doming of the photogrammetric model created by camera distortions. In this study, Tasmania’s northern peatlands were mapped to test the viability of creating hydrological models. The case study was further used to test three different GCP scenarios to assess the effect on DSM quality. From the five scenarios, three required the use of all (16–20) GCPs to create accurate DSMs, whereas the two other sites provided accurate DSMs when only using four GCPs. Hydrological maps produced with the TauDEM tools software package showed high visual accuracy and a good potential for rehabilitation guidance, when using ground- controlled DSMs.

Application of Postprocessing Kinematic Methods with UAS Remote Sensing in Forest Ecosystems

2021 ◽  
Author(s):  
Zachary M Miller ◽  
Joseph Hupy ◽  
Aishwarya Chandrasekaran ◽  
Guofan Shao ◽  
Songlin Fei
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Ground Control ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
Gps Technology ◽  
Ground Control Points ◽  
Forested Areas ◽  
Rapid Deployment ◽  
Aerial Systems

Abstract Unmanned Aerial Systems (UAS) serve as an excellent remote-sensing platform to fulfill an aerial imagery data collection niche previously unattainable in forestry by satellites and manned aircraft. However, for UAS-derived data to be spatially representative, a precise network of ground control points (GCP) is often required, which can be tedious and limit the logistical benefits of UAS rapid deployment capabilities, especially in densely forested areas. Therefore, methods for efficient data collection without GCPs are highly desired in UAS remote sensing. Here, we demonstrate the use of postprocessing kinematic (PPK) technology to obtain subcentimeter precision in datasets of forested areas without the need for placing GCPs. We evaluated two key measures, positional variability and time efficiency, of the PPK technology by comparing them to traditional GCP methods. Results show that PPK displays consistently higher positional precision than traditional GCP approaches. Moreover, PPK surveys and processing take less time to complete than traditional GCP methods and require fewer logistical steps, especially in image acquisition. The time and resource savings with PPK as compared to GCP processing are undeniable. We conclude that PPK technology provides a practical means to produce precise aerial forest surveys. Study Implications Unmanned Aerial Systems (UAS) have enormous potential for lowering costs and streamlining practices in the forestry management and research community. Despite this potential, however, UAS forestry applications have been limited in scope and precision because of a reliance on using ground-based GPS technology to survey ground control points (GCP), which are time intensive and require an open view of the sky. Such a need for a ground-based GCP survey, along with forest canopy serving to limit and scatter incoming GPS signals, diminishes the potential for rapid deployment and precision mapping offered by UAS. Fortunately, Postprocessing-Kinematic (PPK) GPS technology lowers these barriers by providing the means to seamlessly gather highly precise UAS imagery without needing to conduct time-intensive ground-based surveys. This study compares the precision and time-effectiveness between traditional GCP marker surveys and PPK correction methods.

scholarly journals Improving Linear Projects Georeferencing to Create Digital Models Using UAV Imagery

2021 ◽  
Vol 310 ◽  
pp. 04001
Author(s):  
Amr M. Elsheshtawy ◽  
Larisa A. Gavrilova
Keyword(s):  
Global Positioning System ◽  
Accuracy Assessment ◽  
Control Points ◽  
Modified Technique ◽  
Micro Electro Mechanical Systems ◽  
Ground Control Points ◽  
Geometric Precision ◽  
Global Positioning ◽  
Mems Technology ◽  
Relation Model

Global Positioning System (GPS) on Unmanned Aerial Vehicles (UAV) platform relies on Micro Electro Mechanical Systems (MEMS) technology with a precision of 10 m at shooting time at UAV camera stations positions. Nonetheless, obstacles to the GPS signal at the finest flight altitude can prevent accurate camera stations positions retrieval. In this research, three different georeferencing techniques were compared with geometric precision. The first is Direct Georeferencing (DG), which mainly depends on using Navigation GPS onboard without using any Ground Control Points (GCPs). The second is Indirect Georeferencing (IG), which mainly depends on three GCPs used to assist Aero-Triangulation (AT). The third is Modified technique depends on the same three GCPs used in the second method and enhanced location of camera stations usage of the Linear Relation Model (LR Model). The study area was in the south of the Moscow Region, Russia. Threeimaging strips have been taken using the DJI PHANTOM 4 PRO UAV. The accuracy assessment was carried out using image-derived coordinates and checkpoints (CPs) residuals. This study emphasizes that the Modified methodology using enhanced camera stations positions gave better accuracy than using the drone GPS camera stations positions.

Vision-only egomotion estimation in 6DOF using a sky compass

Robotica ◽  
2018 ◽  
Vol 36 (10) ◽  
pp. 1571-1589 ◽  
Author(s):  
Tasarinan Jouir ◽  
Reuben Strydom ◽  
Thomas M. Stace ◽  
Mandyam V. Srinivasan
Keyword(s):  
Global Positioning System ◽  
Closed Loop ◽  
Field Tests ◽  
Estimation Algorithm ◽  
Unmanned Aerial Systems ◽  
Global Positioning ◽  
Egomotion Estimation ◽  
Panoramic Images ◽  
Two Stages ◽  
Aerial Systems

SUMMARYA novel pure-vision egomotion estimation algorithm is presented, with extensions to Unmanned Aerial Systems (UAS) navigation through visual odometry. Our proposed method computes egomotion in two stages using panoramic images segmented into sky and ground regions. Rotations (in 3DOF) are estimated by using a customised algorithm to measure the motion of the sky image, which is affected only by the rotation of the aircraft, and not by its translation. The rotation estimate is then used to derotate the optic flow field generated by the ground, from which the translation of the aircraft (in 3DOF) is estimated by another customised, iterative algorithm. Segmentation of the rotation and translation estimations allows for a partial relaxation of the planar ground assumption, inherently increasing the robustness of the approach. The translation vectors are scaled using stereo-based height to compute the current UAS position through path integration for closed-loop navigation. Outdoor field tests of our approach in a small quadrotor UAS suggest that the technique is comparable to the performance of existing state-of-the-art vision-based navigation algorithms, whilst also removing all dependence on additional sensors, such as an IMU or global positioning system (GPS).

scholarly journals LOW-COST UAS PHOTOGRAMMETRY FOR ROAD INFRASTRUCTURES’ INSPECTION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1145-1150
Author(s):  
L. Pinto ◽  
F. Bianchini ◽  
V. Nova ◽  
D. Passoni
Keyword(s):  
Low Cost ◽  
Laser Scanner ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
The Past ◽  
Ground Control Points ◽  
General Rules ◽  
Aerial Systems ◽  
Cloud Of Points ◽  
Non Destructive

Abstract. All over the world, road infrastructures are getting closer to their life cycle and need to be constantly inspected: a consistent number of bridges are structurally deficient, and the risk of collapse can no longer be excluded. In contrast with the past, the interest in structure durability has recently grown rapidly. In order to make bridges durable, it is necessary to carry out ordinary maintenance, preceded by inspection activities, which can be traditionally divided in two categories: destructive and non-destructive (NDT). All the NDT inspections (visual, IR thermography, GPR) can be conducted by using UAS (Unmanned Aerial Systems), a technology that makes bridges inspections quicker, cheaper, objective and repeatable. This study presents the visual inspection and survey of two bridges by using a UAS DJI Mavic 2 Pro, equipped with a 20Mpixel Hasselblad camera that records 60fps 4K video and a 10bit radiometric resolution. Starting from the acquired data, a 3D model of each structure was built by using Structure from Motion (SfM) principles and software. To validate the two models, each of them characterized by a centimetric accuracy, the UAS camera generated cloud of points and was co-registered with the point cloud of a terrestrial laser-scanner using Ground Control Points (GCPs). To make this, CloudCompare comparison software was used; the plugin M3C2 automatically calculates the distance between the points of two compared clouds. Finally, some general rules concerning the UAS main characteristics for inspection of bridges and software for data processing are proposed.

scholarly journals Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 604
Author(s):  
Carlos A. M. Correia ◽  
Fabio A. A. Andrade ◽  
Agnar Sivertsen ◽  
Ihannah Pinto Guedes ◽  
Milena Faria Pinto ◽  
...  
Keyword(s):  
Reference Frames ◽  
Remote Sensing Data ◽  
Image Sensors ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Whole Process ◽  
Comprehensive Method ◽  
Ground Control Points ◽  
Many Sources ◽  
Aerial Systems

Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location in a geographic frame of reference to the acquired image is a necessary task in the majority of the applications. This process is denominated direct georeferencing when ground control points are not used. Despite it applies simple mathematical fundamentals, the complete direct georeferencing process involves much information, such as camera sensor characteristics, mounting measurements, attitude and position of the UAS, among others. In addition, there are many rotations and translations between the different reference frames, among many other details, which makes the whole process a considerable complex operation. Another problem is that manufacturers and software tools may use different reference frames posing additional difficulty when implementing the direct georeferencing. As this information is spread among many sources, researchers may face difficulties on having a complete vision of the method. In fact, there is absolutely no paper in the literature that explain this process in a comprehensive way. In order to supply this implicit demand, this paper presents a comprehensive method for direct georeferencing of aerial images acquired by cameras mounted on UAS, where all required information, mathematical operations and implementation steps are explained in detail. Finally, in order to show the practical use of the method and to prove its accuracy, both simulated and real flights were performed, where objects of the acquired images were georeferenced.

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