Graduate Thesis Or Dissertation
 

Assessing the Feasibility of Utilizing UAS-Based Point Clouds for Pavement Smoothness/Roughness Quantification

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/7w62fh91q

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  • Transportation agencies continuously strive to improve driving quality and highway safety, which are both highly correlated with the level of smoothness of the road. The International Roughness Index (IRI) is a widely adopted, standardized metric calculated from longitudinal profile data collected on the road. Inertial profilers are devices mounted to vehicles that are commonly utilized by transportation agencies to determine the IRI. However, inertial profilers have a narrow field of view and relatively low positioning accuracy, resulting in a lack of context of the conditions across the road surface. In contrast, remote sensing techniques such as Structure of Motion (SfM) Multi-view Stereopsis MVS) photogrammetry or lidar from an uncrewed aircraft system (UAS) have the ability to efficiently and safely capture detailed 3D texture information across the road surface. Nevertheless, there is still a need to examine the accuracy of determining the pavement roughness (e.g., IRI) with UAS SfM/MVS data. To this end, this study (1) assesses the accuracy of UAS SfM/MVS photogrammetric data, (2) establishes a framework to extract IRI metrics from point cloud data, and (3) explores factors that can impact the quality of point cloud data, such as the flight plan, weather conditions, sensor calibrations, and so forth through a detailed case study.
  • Keywords: International Roughness Index (IRI); UAS-SfM/MVS; Pavement Roughness; Vertical accuracy assessment; Terrestrial lidar, Drones
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  • Pending Publication
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  • 2023-03-04 to 2024-04-04

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