Abstract

This work demonstrates the use of genetic algorithms as a stochastic optimization technique for developing a camera network design and the flight path for photogrammetricapplications using Small Unmanned Aerial Vehicles. This study develops a Virtual Optimizer for Aerial Routes (VOAR) as a new photogrammetric mapping tool for acquisition of images to be used in 3D reconstruction. 3D point cloud models provide detailed information on infrastructure from places where human access may be difficult. This algorithm allows optimized flight paths to monitor infrastructure using GPS coordinates and optimized camera poses ensuring that the set of images captured is improved for 3D point cloud development. Combining optimization techniques, autonomous aircraft and computer vision methods is a new contribution that this work provides.This optimization framework is demonstrated in a real example that includes retrieving the coordinates of the analyzed area and generating autopilot coordinates to operate in fully autonomous mode. These results and their implications are discussed for future work and directions in making optical techniques competitive with aerial or ground based LiDAR systems.

Degree

MS

College and Department

Ira A. Fulton College of Engineering and Technology; Chemical Engineering

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2014-12-01

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd7425

Keywords

UAV, flight planner, optimization, terrain surveillance, photogrammetry, remote sensing, Ivan Rojas

Language

english

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