Ground-Level Surface Reconstruction and Soil Volume Estimation in Construction Sites Using Marching Cubes Method - Fattah Hanafi Sheikhha; Jaho Seo; Hanmin Lee (Applied Sciences)
Accurate environmental sensing is pivotal for advancing automation in construction, particularly in autonomous excavation. Precise 3D representations of complex and dynamic site geometries is essential for obstacle detection, progress monitoring, and safe operation. However, existing sensing techniques often struggle with capturing irregular surfaces and incomplete data in real-time, leading to significant challenges in practical deployment. To address these gaps, we present a novel framework integrating curve approximation, surface reconstruction, and marching cubes algorithm to transform raw sensor data into a detailed and computationally efficient soil surface representation. Our approach improves site modeling accuracy, paving the way for reliable and efficient construction automation. This paper enhances sensory data quality using surface reconstruction techniques, followed by the marching cubes algorithm to generate an accurate and flexible 3D soil model. This model facilitates rapid estimation of soil volume, weight, and shape, offering an efficient approach for environmental analysis and decision-making in automated systems. Experimental validation demonstrated the effectiveness of the proposed method, achieving relative errors of 4.92% and 1.42% across two excavation cycles. Additionally, the marching cubes algorithm completed volume estimation in just 0.05 s, confirming the approach’s accuracy and suitability for real-time applications in dynamic construction environments.