LOW-COST PHOTOGRAMMETRY SYSTEM FOR GEOREFERENCED STRUCTURE FROM MOTION
Hill, Nicholas Benjamin
Photogrammetry has become increasingly available as a tool for re-constructing the 3D model of real-world objects and terrain, which is referred to as the structure from motion (SFM) solution. The re-constructed model has three-dimensional colorized surfaces. Integration of geo-referenced data into SFM allows for precise geo-registration, scaling and orienting of the object. Today, industrial users of SFM typically acquire imagery from unmanned aerial vehicles (UAVs). UAV-based SFM relies on having a large number of ground control points that were surveyed ahead of time as the main source of geo-referenced data. The deployment and survey of ground control points are time consuming, and sometimes infeasible due to environmental constraints. A better solution is to gather location and/or orientation data of the camera in flight. A capable navigation device can record the precise location and orientation of the camera at the exact moment at which every image was taken. With that information, few or no ground control points are needed for geo-registration. However, the commercially available solutions of UAV-based SFM with an on-board navigator tend to be bulky and expensive. A low-cost, compact solution with open interfaces will be proposed in this work.
Hill, Nicholas Benjamin. (July 2022). LOW-COST PHOTOGRAMMETRY SYSTEM FOR GEOREFERENCED STRUCTURE FROM MOTION (Master's Thesis, East Carolina University). Retrieved from the Scholarship. (http://hdl.handle.net/10342/11098.)
Hill, Nicholas Benjamin. LOW-COST PHOTOGRAMMETRY SYSTEM FOR GEOREFERENCED STRUCTURE FROM MOTION. Master's Thesis. East Carolina University, July 2022. The Scholarship. http://hdl.handle.net/10342/11098. October 02, 2022.
Hill, Nicholas Benjamin, “LOW-COST PHOTOGRAMMETRY SYSTEM FOR GEOREFERENCED STRUCTURE FROM MOTION” (Master's Thesis., East Carolina University, July 2022).
Hill, Nicholas Benjamin. LOW-COST PHOTOGRAMMETRY SYSTEM FOR GEOREFERENCED STRUCTURE FROM MOTION [Master's Thesis]. Greenville, NC: East Carolina University; July 2022.
East Carolina University