pacific nw transportation consortium region 10 Pactrans

Assessing the Feasibility of Utilizing UAS-based Point Cloud in Pavement Smoothness/Roughness Measurement

PI: Ezrhuo Che (OSU), chee@oregonstate.edu, ORCID: 0000-0001-7664-8098

Co PIs: none

AMOUNT & MATCH: $40,000 from PacTrans; $40,000 Match

PERFORMANCE PERIOD: 3/16/2021 – 3/15/2022

STATUS: Completed

CATEGORIES: Pavement, Roughness, UAS

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DESCRIPTION: UAS approaches do have limitations as predicting the accuracy and quality of the UAS-based point cloud data can be very challenging and complicated as the resulting data can be affected by many factors (e.g., sensor calibration, flight plan, system specifications, texture of the surface, lighting conditions, ground control, processing algorithms/software, etc.). UAS-based point clouds typically have a higher uncertainty than lidar-based point clouds, especially for the areas with a low surface texture or poor lighting. Thus, it is important to report the absolute or relative uncertainty with the roughness measurements such that they can be combined with or compared. Considering the limitations, there needs to be a rigorous accuracy assessment to validate the feasibility of utilizing UAS data to evaluate pavement roughness. In addition, because there are few guidelines or standards in UAS data collection and processing workflows in general, it is critical to have application- or case-oriented guidelines available to ensure the data satisfies the accuracy requirements of the project. This project will develop a framework to obtain pavement roughness metrics (e.g., IRI) from UAS acquired lidar and structure from motion point clouds, validate the viability of assessing pavement roughness using UAS-based point cloud data, and provide general guidelines for UAS data collection and processing targeting extraction of pavement information.