Automatic Modeling Framework of Existing Road Based on Point Cloud Mapping via Low-Cost UAV-LiDAR System

The increasing interest in Digital Twins (DT) for road infrastructure stems from their ability to seamlessly integrate data from various sources and facilitate interactions between physical entities and their virtual counterparts. The cornerstone for achieving a road digital twin is a high-quality road model. Despite the widespread application of Building Information Modeling (BIM) technology across various stages of road engineering, the modeling of existing roads within the BIM framework has faced challenges, including a lack of accurate design data and extensive manual work. This paper presents an innovative and cost-effective approach to model existing roads by combining Uncrewed Airborne Vehicles (UAVs) - LiDAR System (ULS) and BIM. It begins with the development of a low-cost UAV-LiDAR platform designed for efficient data collection in road environments, capturing essential geometric information. Subsequently, point cloud mapping techniques, including an intensity-based planar downsampling strategy and a hierarchical refinement method, are employed to create accurate point cloud maps using the cost-effective ULS. Further, an automated modeling pipeline is proposed with point cloud processing and Revit Dynamo. It comprises three main steps: cross-section model design, point cloud parameter extraction, and the design of an automated workflow in Dynamo. The proposed method is validated with both qualitative and quantitative experiments, and the results demonstrate that the generated model accurately represents the structural, alignment, and lateral gradient details of the experimental road. In summary, this study offers a cost-efficient yet highly effective means to construct realistic models of existing roads.