Depth-camera-based rebar detection and digital reconstruction for robotic concrete spraying

Abstract

In this paper, we tackle the challenge of detection and accurate digital reconstruction of steel rebar meshes using a set of industrial depth cameras. A construction example under investigation in this paper is robotic concrete spraying, where material is sprayed onto double-curved single layered rebar meshes. Before the spraying process can start, the location and geometry of the rebar mesh needs to be accurately know. We present an automatic image-based processing approach of depth images for grid point extraction at an accuracy of a few mm. Furthermore, we propose a sequence of execution steps in a robotic setup, including the hand–eye calibration, which enables the direct georeferencing of multiple data sets acquired from various poses into a common coordinate system. With the proposed approach we are able to digitally reconstruct a mesh of an unknown geometry in under 10 min with an accuracy better than 5 mm. The digitally reconstructed mesh allows for computation of material needed for its construction, enabling sustainable use of concrete in digital fabrication. The accurately reconstructed digital mesh, generated based on the proposed approach in this paper, is the input for the following spraying step, allowing for generation of accurate spray trajectories.