Assessing current infrastructure condition of road is costly, complex, logistically demanding and requires extensive staff training and extensive data processing. Current road condition assessment method, typically generate International Roughness Index (IRI) data to estimate type classification of road, and handling volume estimation is not yet done. LiDAR smartphone, opens up new possibilities for low-cost scientific applications. The proposed road condition assessment method in this article is the LiDAR Smartphone and low-cost GNSS Model. This model is expected to shorten survey time, reduce survey costs, facilitate implementation during field investigation and post-processing. Smartphone-based devices and applications not only save costs, but also provide the convenience of in data collection and the ability to assess road conditions in real time and identify pavement damage caused by cracks of various severity. The benefit of generating a three-dimensional model of the nearby environment without any prior preparation enables immediate and timely collection of data. Direct visualization of the captured surfaces and processing the data together minimizes the need for hardware and software, making it more cost-effective than current surveying techniques for determining road condition. LiDAR Smartphone and Low-cost GNSS models is a more economical and efficient option in term of cost and time to existing road surface measurement methods and offer common device availability, accessibility, ease of use, comfortable handling, and integrated data processing. Enhancements in the export of raw point cloud data, maximum size of scan, and overall coverage are inevitable for new software applications.

Original languageEnglish
Article number080022
JournalAIP Conference Proceedings
Issue number1
Publication statusPublished - 18 Mar 2024
Event7th International Conference on Science and Technology: Smart Innovation Research on Science and Technology for a Better Life, ICST 2022 - Hybrid, Mataram City, Indonesia
Duration: 14 Nov 2022 → …


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