Terrain Surface Interpolation from Large-Scale 3D Point Cloud Data with Semantic Segmentation in Earthwork Sites

Over the past few years, various research has been conducted to utilize 3D point cloud data in construction sites. This is because 3D point cloud data contain a variety of information, such as spatial coordinates (X, Y, Z), intensity, and color (RGB), making them highly applicable to construction environments that require precise operations. Accordingly, this research developed a new terrain surface interpolation method that leverages diverse information embedded in large-scale 3D point cloud data acquired from earthwork sites, as part of a foundational study for construction automation. In particular, the proposed terrain surface interpolation method was designed to be integrated with semantic segmentation based on 3D point cloud data, with a focus on enhancing the accuracy of earthwork volume estimation. Furthermore, field experiments were conducted using heavy construction equipment to compare terrain change and earthwork volume analyses between 3D point cloud data with and without the application of the proposed interpolation method. The analysis results of earthwork volumes indicated that the application of the terrain interpolation method to 3D point cloud data for construction equipment reduced estimation errors by approximately 94% compared to non-interpolated data. These findings demonstrate the effectiveness of the proposed method and are expected to contribute to future research in artificial intelligence and robotics utilizing 3D point cloud data within the construction industry.