Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck...

Area of Science:

• Geomatics Engineering
• Photogrammetry
• Remote Sensing

Background:

• Unmanned Aerial Vehicles (UAVs) are increasingly used for generating 3D point cloud models.
• Data quality is contingent upon flight parameters like altitude, camera angle, and overlap rate.
• Current methods often involve redundant data collection, increasing time and processing demands.

Purpose of the Study:

• To investigate the relationship between UAV flight parameters and 3D point cloud model quality.
• To propose and apply a suite of seven metrics for evaluating data quality and efficiency.
• To determine optimal flight parameters for various data collection objectives.

Main Methods:

• Utilized imagery from Unmanned Aerial Vehicles (UAVs) for 3D point cloud generation.
• Proposed seven metrics: total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency.
• Field-tested data collection strategies on a full-scale structure.

Main Results:

• UAV altitude and camera angle were primary drivers of data density and uniformity.
• A minimum 66% overlap rate was identified as necessary for successful 3D reconstruction.
• Multiple flight paths enhanced local geometric accuracy more than increased overlap; 77% overlap yielded highest coverage due to processing artefacts.

Conclusions:

• No single set of UAV flight parameters is optimal for all data collection goals.
• Understanding the impact of flight path parameters is critical for efficient and high-quality UAV data acquisition.
• Strategic flight planning is essential for maximizing the utility of UAV-derived 3D models.