scholarly journals Automated Aerial Triangulation for UAV-Based Mapping

2018 ◽  
Vol 10 (12) ◽  
pp. 1952 ◽  
Author(s):  
Fangning He ◽  
Tian Zhou ◽  
Weifeng Xiong ◽  
Seyyed Hasheminnasab ◽  
Ayman Habib
Keyword(s):  
Bundle Adjustment ◽  
Superior Performance ◽  
Commercial Software ◽  
Aerial Triangulation ◽  
Ground Control Points ◽  
Estimated Parameters ◽  
Aerial Vehicle ◽  
Initial Recovery ◽  
Uav Images ◽  
Orientation Parameters

Accurate 3D reconstruction/modelling from unmanned aerial vehicle (UAV)-based imagery has become the key prerequisite in various applications. Although current commercial software has automated the process of image-based reconstruction, a transparent system, which can be incorporated with different user-defined constraints, is still preferred by the photogrammetric research community. In this regard, this paper presents a transparent framework for the automated aerial triangulation of UAV images. The proposed framework is conducted in three steps. In the first step, two approaches, which take advantage of prior information regarding the flight trajectory, are implemented for reliable relative orientation recovery. Then, initial recovery of image exterior orientation parameters (EOPs) is achieved through either an incremental or global approach. Finally, a global bundle adjustment involving Ground Control Points (GCPs) and check points is carried out to refine all estimated parameters in the defined mapping coordinate system. Four real image datasets, which are acquired by two different UAV platforms, have been utilized to evaluate the feasibility of the proposed framework. In addition, a comparative analysis between the proposed framework and the existing commercial software is performed. The derived experimental results demonstrate the superior performance of the proposed framework in providing an accurate 3D model, especially when dealing with acquired UAV images containing repetitive pattern and significant image distortions.

scholarly journals Evaluation of the Georeferencing Accuracy of a Photogrammetric Model Using a Quadrocopter with Onboard GNSS RTK

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2318 ◽  
Author(s):  
Martin Štroner ◽  
Rudolf Urban ◽  
Tomáš Reindl ◽  
Jan Seidl ◽  
Josef Brouček
Keyword(s):  
Vertical Component ◽  
Satellite System ◽  
Bundle Adjustment ◽  
Ground Control ◽  
Control Points ◽  
Ground Sample ◽  
Ground Control Points ◽  
Aerial Vehicle ◽  
Global Navigation Satellite ◽  
Orientation Parameters

Using a GNSS RTK (Global Navigation Satellite System Real Time Kinematic) -equipped unmanned aerial vehicle (UAV) could greatly simplify the construction of highly accurate digital models through SfM (Structure from Motion) photogrammetry, possibly even avoiding the need for ground control points (GCPs). As previous studies on this topic were mostly performed using fixed-wing UAVs, this study aimed to investigate the results achievable by a quadrocopter (DJI Phantom 4 RTK). Three image acquisition flights were performed for two sites of a different character (urban and rural) along with three calculation variants for each flight: georeferencing using ground-surveyed GCPs only, onboard GNSS RTK only, and a combination thereof. The combined and GNSS RTK methods provided the best results (at the expected level of accuracy of 1–2 GSD (Ground Sample Distance)) for both the vertical and horizontal components. The horizontal positioning was also accurate when georeferencing directly based on the onboard GNSS RTK; the vertical component, however, can be (especially where the terrain is difficult for SfM evaluation) burdened with relatively high systematic errors. This problem was caused by the incorrect identification of the interior orientation parameters calculated, as is customary for non-metric cameras, together with bundle adjustment. This problem could be resolved by using a small number of GCPs (at least one) or quality camera pre-calibration.

scholarly journals A PHOTOGRAMMETRY-BASED STRUCTURE FROM MOTION ALGORITHM USING ROBUST ITERATIVE BUNDLE ADJUSTMENT TECHNIQUES

2018 ◽  
Vol IV-4/W6 ◽  
pp. 73-80 ◽  
Author(s):  
S. Verykokou ◽  
C. Ioannidis
Keyword(s):  
Structure From Motion ◽  
Image Matching ◽  
Template Matching ◽  
Bundle Adjustment ◽  
Geometric Constraints ◽  
Exterior Orientation ◽  
Commercial Software ◽  
Ground Control Points ◽  
Oblique Images ◽  
Orientation Parameters

<p><strong>Abstract.</strong> The purpose of this paper is the presentation of a novel algorithm for automatic estimation of the exterior orientation parameters of image datasets, which can be applied in the case that the scene depicted in the images has a planar surface (e.g., roof of a building). The algorithm requires the measurement of four coplanar ground control points (GCPs) in only one image. It uses a template matching method combined with a homography-based technique for transfer of the GCPs in another image, along with an incremental photogrammetry-based Structure from Motion (SfM) workflow, coupled with robust iterative bundle adjustment methods that reject any remaining outliers, which have passed through the checks and geometric constraints imposed during the image matching procedure. Its main steps consist of (i) determination of overlapping images without the need for GPS/INS data; (ii) image matching and feature tracking; (iii) estimation of the exterior orientation parameters of a starting image pair; and (iv) photogrammetry-based SfM combined with iterative bundle adjustment methods. A developed software solution implementing the proposed algorithm was tested using a set of UAV oblique images. Several tests were performed for the assessment of the errors and comparisons with well-established commercial software were made, in terms of automation and correctness of the computed exterior orientation parameters. The results show that the estimated orientation parameters via the proposed solution have comparable accuracy with those ones computed through the commercial software using the highest possible accuracy settings; in addition, double manual work was required by the commercial software compared to the proposed solution.</p>

scholarly journals Efficient SfM for Oblique UAV Images: From Match Pair Selection to Geometrical Verification

2018 ◽  
Vol 10 (8) ◽  
pp. 1246 ◽  
Author(s):  
San Jiang ◽  
Wanshou Jiang
Keyword(s):  
Feature Matching ◽  
Bundle Adjustment ◽  
Imaging Systems ◽  
Match Pair ◽  
Software Packages ◽  
Aerial Vehicle ◽  
Selection For ◽  
Image Pairs ◽  
Uav Images ◽  
Reliable Solution

Accurate orientation is required for the applications of UAV (Unmanned Aerial Vehicle) images. In this study, an integrated Structure from Motion (SfM) solution is proposed, which aims to address three issues to ensure the efficient and reliable orientation of oblique UAV images, including match pair selection for large-volume images with large overlap degree, reliable feature matching of images captured from varying directions, and efficient geometrical verification of initial matches. By using four datasets captured with different oblique imaging systems, the proposed SfM solution is comprehensively compared and analyzed. The results demonstrate that linear computational costs can be achieved in feature extraction and matching; although high decrease ratios occur in image pairs, reliable orientation results are still obtained from both the relative and absolute bundle adjustment (BA) tests when compared with other software packages. For the orientation of oblique UAV images, the proposed method can be an efficient and reliable solution.

scholarly journals AUTOMATIC KAPPA ANGLE ESTIMATION FOR AIR PHOTOS BASED ON PHASE ONLY CORRELATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 131-135
Author(s):  
Z. Xiong ◽  
D. Stanley ◽  
Y. Xin
Keyword(s):  
Bundle Adjustment ◽  
Ground Control ◽  
Correlation Technique ◽  
Control Points ◽  
Initial Value ◽  
Ground Control Points ◽  
Attitude Angle ◽  
Camera Position ◽  
Air Photo ◽  
Orientation Parameters

The approximate value of exterior orientation parameters is needed for air photo bundle adjustment. Usually the air borne GPS/IMU can provide the initial value for the camera position and attitude angle. However, in some cases, the camera’s attitude angle is not available due to lack of IMU or other reasons. In this case, the kappa angle needs to be estimated for each photo before bundle adjustment. The kappa angle can be obtained from the Ground Control Points (GCPs) in the photo. Unfortunately it is not the case that enough GCPs are always available. In order to overcome this problem, an algorithm is developed to automatically estimate the kappa angle for air photos based on phase only correlation technique. This function has been embedded in PCI software. Extensive experiments show that this algorithm is fast, reliable, and stable.

scholarly journals Evaluating the Potential of PPK Direct Georeferencing for UAV-SfM Photogrammetry and Precise Topographic Mapping

2019 ◽  
Author(s):  
He Zhang ◽  
Emilien Aldana-Jague ◽  
François Clapuyt ◽  
Florian Wilken ◽  
Veerle Vanacker ◽  
...  
Keyword(s):  
Focal Length ◽  
Positional Accuracy ◽  
Ground Control Points ◽  
Time Period ◽  
Surface Models ◽  
Flight Height ◽  
Aerial Vehicle ◽  
Repeatability And Reproducibility ◽  
Uav Images ◽  
Topography Data

Abstract. Images captured by Unmanned aerial vehicle (UAV) and processed by Structure from Motion (SfM) photogrammetry are increasingly used in geomorphology to obtain high resolution topography data. Conventional georeferencing using ground control points (GCPs) provides reliable positioning but the geometrical accuracy critically depends on the number and spatial layout of the GCPs. This limits the time- and cost-effectiveness. Direct georeferencing of the UAV images with differential GNSS, such as PPK (Post-Processing Kinematic), may overcome these limitations by providing accurate and directly georeferenced surveys. To investigate the positional accuracy, repeatability and reproducibility of digital surface models (DSMs) generated by a UAV-PPK-SfM workflow, we carried out multiple flight missions with different camera/UAV systems. Our analysis showed that the PPK solution provides the same accuracy (mean: ca. 0.01 m, RMSE: ca. 0.03 m) as the GCP method. Furthermore, our results indicated that camera properties (i.e., focal length, resolution, sensor quality) have an impact on the accuracy but planimetric and altimetric errors remained in the range of 0.011 to 0.024 m. By analysing the repeatability of DSM construction over a time period of a few months, our study demonstrates that a UAV-PPK-SfM workflow can provide consistent, repeatable 4D data with an accuracy of a few centimetres without the use of GCPs. An uncertainty analysis showed that the minimum level of topographical change detection was ca. ±0.04 m for a high-end DSLR camera and ca. ±0.08 m for an action camera (for a flight height of 45 m). The level of detection substantially improved when reducing the UAV flight height. This study demonstrates the repeatability, reproducibility and efficiency of a PPK-SfM workflow in the context of 4D earth surface monitoring with time-laps SfM photogrammetry. As such, it should be considered as an efficient tool to monitor geomorphic processes accurately and quickly at a very high spatial and temporal resolution.

scholarly journals Rectification of Bowl-Shape Deformation of Tidal Flat DEM derived from UAV Imaging

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1602 ◽  
Author(s):  
Hyoseong Lee ◽  
Dongyeob Han
Keyword(s):  
Tidal Flat ◽  
Tidal Flats ◽  
Control Points ◽  
Matching Method ◽  
Height Difference ◽  
Ground Control Points ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Rotation Parameters ◽  
Uav Images

It is necessary to periodically obtain topographic maps of the geographical and environmental characteristics of tidal flats to systemically manage and monitor them. Accurate digital elevation models (DEMs) of the tidal flats are produced while using ground control points (GCPs); however, it is both complicated and difficult to conduct GPS surveys and readings of image coordinates that correspond to these because tidal flat areas are not easy to access. The position and distribution of GCPs affect DEMs, because the entire working area cannot be covered during a survey. In this study, a least-squares height-difference (LHD) DEM matching method with a polynomial model is proposed to increase the number of DEM grids while using a presecured precise DEM to rectify the distortion and bowl effect produced by unmanned aerial vehicle (UAV) images. The most appropriate result was obtained when the translation parameters were quadratic curve polynomials with an increasing number of grids and the rotation parameters were constant. The experimental results indicated that the proposed method reduced the distortion and eliminated the error caused by the bowl effect while only using a reference DEM.

scholarly journals Accuracy of Unmanned Aerial Vehicle (UAV) and SfM Photogrammetry Survey as a Function of the Number and Location of Ground Control Points Used

2018 ◽  
Vol 10 (10) ◽  
pp. 1606 ◽  
Author(s):  
Enoc Sanz-Ablanedo ◽  
Jim Chandler ◽  
José Rodríguez-Pérez ◽  
Celestino Ordóñez
Keyword(s):  
Critical Role ◽  
Bundle Adjustment ◽  
Ground Control ◽  
Control Points ◽  
Ground Sample ◽  
Ground Control Points ◽  
Check Points ◽  
Aerial Vehicle

The geometrical accuracy of georeferenced digital surface models (DTM) obtained from images captured by micro-UAVs and processed by using structure from motion (SfM) photogrammetry depends on several factors, including flight design, camera quality, camera calibration, SfM algorithms and georeferencing strategy. This paper focusses on the critical role of the number and location of ground control points (GCP) used during the georeferencing stage. A challenging case study involving an area of 1200+ ha, 100+ GCP and 2500+ photos was used. Three thousand, four hundred and sixty-five different combinations of control points were introduced in the bundle adjustment, whilst the accuracy of the model was evaluated using both control points and independent check points. The analysis demonstrates how much the accuracy improves as the number of GCP points increases, as well as the importance of an even distribution, how much the accuracy is overestimated when it is quantified only using control points rather than independent check points, and how the ground sample distance (GSD) of a project relates to the maximum accuracy that can be achieved.

scholarly journals Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4092 ◽  
Author(s):  
Xiongzhe Han ◽  
J. Alex Thomasson ◽  
G. Cody Bagnall ◽  
N. Ace Pugh ◽  
David W. Horne ◽  
...  
Keyword(s):  
Plant Height ◽  
Ground Truth ◽  
Ground Level ◽  
Strong Relationship ◽  
Future Application ◽  
Ground Control Points ◽  
Global Demand ◽  
Sorghum Plant ◽  
Aerial Vehicle ◽  
Uav Images

Continuing population growth will result in increasing global demand for food and fiber for the foreseeable future. During the growing season, variability in the height of crops provides important information on plant health, growth, and response to environmental effects. This paper indicates the feasibility of using structure from motion (SfM) on images collected from 120 m above ground level (AGL) with a fixed-wing unmanned aerial vehicle (UAV) to estimate sorghum plant height with reasonable accuracy on a relatively large farm field. Correlations between UAV-based estimates and ground truth were strong on all dates (R2 > 0.80) but are clearly better on some dates than others. Furthermore, a new method for improving UAV-based plant height estimates with multi-level ground control points (GCPs) was found to lower the root mean square error (RMSE) by about 20%. These results indicate that GCP-based height calibration has a potential for future application where accuracy is particularly important. Lastly, the image blur appeared to have a significant impact on the accuracy of plant height estimation. A strong correlation (R2 = 0.85) was observed between image quality and plant height RMSE and the influence of wind was a challenge in obtaining high-quality plant height data. A strong relationship (R2 = 0.99) existed between wind speed and image blurriness.

scholarly journals Generating a High-Precision True Digital Orthophoto Map Based on UAV Images

2018 ◽  
Vol 7 (9) ◽  
pp. 333 ◽  
Author(s):  
Yu Liu ◽  
Xinqi Zheng ◽  
Gang Ai ◽  
Yi Zhang ◽  
Yuqiang Zuo
Keyword(s):  
High Precision ◽  
Image Texture ◽  
Surface Model ◽  
Ground Sample ◽  
Planning And Design ◽  
Ground Control Points ◽  
Sensing Technology ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Digital Orthophoto

Unmanned aerial vehicle (UAV) low-altitude remote sensing technology has recently been adopted in China. However, mapping accuracy and production processes of true digital orthophoto maps (TDOMs) generated by UAV images require further improvement. In this study, ground control points were distributed and images were collected using a multi-rotor UAV and professional camera, at a flight height of 160 m above the ground and a designed ground sample distance (GSD) of 0.016 m. A structure from motion (SfM), revised digital surface model (DSM) and multi-view image texture compensation workflow were outlined to generate a high-precision TDOM. We then used randomly distributed checkpoints on the TDOM to verify its precision. The horizontal accuracy of the generated TDOM was 0.0365 m, the vertical accuracy was 0.0323 m, and the GSD was 0.0166 m. Tilt and shadowed areas of the TDOM were eliminated so that buildings maintained vertical viewing angles. This workflow produced a TDOM accuracy within 0.05 m, and provided an effective method for identifying rural homesteads, as well as land planning and design.

scholarly journals Linear Approach for Initial Recovery of the Exterior Orientation Parameters of Randomly Captured Images by Low-Cost Mobile Mapping Systems

2014 ◽  
Vol XL-1 ◽  
pp. 149-154 ◽  
Author(s):  
F. He ◽  
A. Habib
Keyword(s):  
Relative Orientation ◽  
Bundle Adjustment ◽  
Adjustment Process ◽  
Coordinate Frame ◽  
Exterior Orientation ◽  
Linear Solution ◽  
Linear Approach ◽  
Initial Recovery ◽  
Orientation Parameters ◽  
Local Coordinate Frame

In this paper, we present a novel linear approach for the initial recovery of the exterior orientation parameters (EOPs) of images. Similar to the conventional Structure from Motion (SfM) algorithm, the proposed approach is based on a two-step strategy. In the first step, the relative orientation of all possible image stereo-pairs is estimated. In the second step, a local coordinate frame is established, and an incremental image augmentation process is implemented to reference all the remaining images into a local coordinate frame. Since our approach is based on a linear solution for both the relative orientation estimation as well as the initial recovery of the image EOPs, it does not require any initial approximation for the optimization process. Another advantage of our approach is that it does not require any prior knowledge regarding the sequence of the image collection procedure, therefore, it can handle a set of randomly collected images in the absence of GNSS/INS information. In order to illustrate the feasibility of our approach, several experimental tests are conducted on real datasets captured in either a block or linear trajectory configuration. The results demonstrate that the initial image EOPs obtained are accurate and can serve as a good initialization for an additional bundle adjustment process.

Sign in / Sign up

Export Citation Format

Share Document