Automatic Object Recognition and Registration of Dynamic Construction Equipment from a 3D Point Cloud

This paper introduces a model-based automatic object recognition and registration framework to assist heavy equipment operators in rapidly perceiving 3D working environment at dynamic construction sites. A video camera and a laser scanner were utilized in this study to rapidly recognize and register dynamic 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 scan data updating method has been developed which only updates the dynamic target object’s point cloud data while keeping the previously scanned static work environments. Extracted target areas containing 3D point clouds were orthographically projected into a series of 2D planes with a rotation center located in the target’s vertical-middle line. Prepared 2D templates were compared to these 2D planes by extracting SURF features. Point cloud bundles of the target were recognized, and followed by the prepared CAD model’s registration to the templates. The field experimental results show that the proposed rapid workspace modeling method can significantly improve heavy construction equipment operations and automated equipment control by rapidly modeling dynamic target objects in a 3D view.