Smart scanning and near real-time 3D surface modeling of dynamic construction equipment from a point cloud

Abstract This paper introduces a framework of automatic object recognition and rapid surface modeling to aid the heavy equipment operation in rapidly perceiving 3D working environment at dynamic construction sites. A custom-designed data acquisition system was employed in this study to rapidly recognize the selected target objects in a 3D space by dynamically separating target object’s point cloud data from a background scene for a quick computing process. A smart scanning method was also applied to only update the target object’s point cloud data while keeping the previously scanned static work environments. Then the target’s point cloud data were rapidly converted into a 3D surface model using the concave hull surface modeling algorithm after a process of data filtering and downsizing to increase the model accuracy and data processing speed. The performance of the proposed framework was tested with two different heavy equipment types at a steel frame building construction site. The generated surface model and the point cloud of static surroundings were wirelessly presented to a remote operator. The field test results show that the proposed rapid target surface modeling method would significantly improve productivity and safety in heavy construction equipment operations by distinguishing a dynamic target object from a surrounding static environment in 3D views in near real time.