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

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-...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-07, Vol.12 (13), p.2169
Main Authors: Arce, Samuel, Vernon, Cory A., Hammond, Joshua, Newell, Valerie, Janson, Joseph, Franke, Kevin W., Hedengren, John D.
Format: Article
Language:English
Subjects:
Algorithms
automated inspection
Automation
Autonomous underwater vehicles
Cameras
Clustering
Computer simulation
Genetic algorithms
Genetic analysis
Inspection
iterative inspection
Iterative methods
Learning algorithms
Machine learning
Methods
multi-scale
Municipal water
Optimization
Photogrammetry
Pixels
Planning
Principal components analysis
Quality
Regression analysis
Remote sensing
Structure-from-Motion
Three dimensional models
Traveling salesman problem
Unmanned Aerial Vehicles
view-planning
Water tanks
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12132169