Developing structure-from-motion models from applied streetview and UAV images

2020 ◽  
Author(s):  
Harriet Wright
Keyword(s):  
Structure From Motion ◽  
Motion Models ◽  
Uav Images

scholarly journals Automated 3D Reconstruction Using Optimized View-Planning Algorithms for Iterative Development of Structure-from-Motion Models

2020 ◽  
Vol 12 (13) ◽  
pp. 2169 ◽  
Author(s):  
Samuel Arce ◽  
Cory A. Vernon ◽  
Joshua Hammond ◽  
Valerie Newell ◽  
Joseph Janson ◽  
...  
Keyword(s):  
Structure From Motion ◽  
Principal Component ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Water Tank ◽  
View Planning ◽  
Motion Models ◽  
Novel Approach ◽  
Photogrammetric Method ◽  
Next Best View

Unsupervised machine learning algorithms (clustering, genetic, and principal component analysis) automate Unmanned Aerial Vehicle (UAV) missions as well as the creation and refinement of iterative 3D photogrammetric models with a next best view (NBV) approach. The novel approach uses Structure-from-Motion (SfM) to achieve convergence to a specified orthomosaic resolution by identifying edges in the point cloud and planning cameras that “view” the holes identified by edges without requiring an initial model. This iterative UAV photogrammetric method successfully runs in various Microsoft AirSim environments. Simulated ground sampling distance (GSD) of models reaches as low as 3.4 cm per pixel, and generally, successive iterations improve resolution. Besides analogous application in simulated environments, a field study of a retired municipal water tank illustrates the practical application and advantages of automated UAV iterative inspection of infrastructure using 63 % fewer photographs than a comparable manual flight with analogous density point clouds obtaining a GSD of less than 3 cm per pixel. Each iteration qualitatively increases resolution according to a logarithmic regression, reduces holes in models, and adds details to model edges.

scholarly journals Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection

2019 ◽  
Vol 7 (3) ◽  
pp. 807-827 ◽  
Author(s):  
He Zhang ◽  
Emilien Aldana-Jague ◽  
François Clapuyt ◽  
Florian Wilken ◽  
Veerle Vanacker ◽  
...  
Keyword(s):  
Change Detection ◽  
Structure From Motion ◽  
Low Cost ◽  
Post Processing ◽  
Positional Accuracy ◽  
Surface Change ◽  
Ground Control Points ◽  
Single Lens ◽  
Surface Representations ◽  
Uav Images

Abstract. Images captured by unmanned aerial vehicles (UAVs) 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 two different camera–UAV systems: a small-form low-cost micro-UAV equipped with a high field of view (FOV) action camera and a professional UAV equipped with a digital single lens reflex (DSLR) camera. Our analysis showed that the PPK solution provides the same accuracy (MAE: ca. 0.02 m, RMSE: ca. 0.03 m) as the GCP method for both UAV systems. Our study demonstrated that a UAV–PPK–SfM workflow can provide consistent, repeatable 4-D data with an accuracy of a few centimeters. However, a few flights showed vertical bias and this could be corrected using one single GCP. We further evaluated different methods to estimate DSM uncertainty and show that this has a large impact on centimeter-level topographical change detection. The DSM reconstruction and surface change detection based on a DSLR and action camera were reproducible: the main difference lies in the level of detail of the surface representations. The PPK–SfM workflow in the context of 4-D Earth surface monitoring should be considered an efficient tool to monitor geomorphic processes accurately and quickly at a very high spatial and temporal resolution.

High-Resolution Structure-From-Motion Models and Orthophotos of the Southern Sections of the 2019 Mw 7.1 and 6.4 Ridgecrest Earthquakes Surface Ruptures

2020 ◽  
Vol 91 (4) ◽  
pp. 2124-2126 ◽  
Author(s):  
Ian Pierce ◽  
Alana Williams ◽  
Richard D. Koehler ◽  
Colin Chupik
Keyword(s):  
High Resolution ◽  
Structure From Motion ◽  
Point Cloud ◽  
Earthquake Sequence ◽  
Aerial Photographs ◽  
Motion Models ◽  
Cloud Models ◽  
High Resolution Structure ◽  
Surface Ruptures ◽  
Resolution Structure

Abstract Aerial photographs were collected in the days immediately following the 4–5 July 2019 Ridgecrest earthquake sequence (e.g., Barnhart et al., 2019) along the publically accessible sections of the surface ruptures south of California 178. These photos were then used to produce structure-from-motion point cloud models and orthophotos with resolutions varying from ∼1 to 20  cm/pixel. Here, the models are released and initial observations of the nature of the surface ruptures are presented.

scholarly journals Structure from motion (SfM) processing of UAV images and combination with terrestrial laser scanning, applied for a 3D-documentation in a hazardous situation

2017 ◽  
Vol 8 (2) ◽  
pp. 1492-1504 ◽  
Author(s):  
Isabel Martínez-Espejo Zaragoza ◽  
Gabriella Caroti ◽  
Andrea Piemonte ◽  
Björn Riedel ◽  
Dieter Tengen ◽  
...  
Keyword(s):  
Structure From Motion ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
3D Documentation ◽  
Uav Images
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