Paper Info
Title
Automated 3D Reconstruction Using Optimized View-Planning Algorithms for Iterative Development of Structure-from-Motion Models.
Abstract
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 as3.4cm 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.
Year
DOI
Venue
2020
10.3390/rs12132169
REMOTE SENSING
Keywords
DocType
Volume
Structure-from-Motion,Unmanned Aerial Vehicles,iterative inspection,automated inspection,multi-scale,view-planning,unsupervised machine learning,autonomous flight,iterative optimization
Journal
12
Issue
Citations 
PageRank 
13
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Samuel Arce100.68
Cory Vernon200.68
Joshua Hammond300.68
Valerie Newell400.68
Joseph Janson500.68
Kevin Franke601.01
John D. Hedengren7548.20