This paper presents a feasibility study of surface geometry (SG) evaluation and material classification (MC) for robotic spraying. We propose two complementary approaches using point clouds and intensity data provided by a state-of-the-art industrial time-of-flight (ToF) depth camera. The SG evaluation is based on geometric feature computation within local neighbourhoods, which are then used within a supervised classification. The results of this approach are SG classes according to the level of geometric variability of the surface, displayed as SG maps. For MC, active reflectance estimation is investigated and exploited to derive features related to the reflectance and diffusive properties of each material for classification. The result of both approaches can be prospectively used as feedback in digital fabrication for in-line adaptation of the process to improve control of relevant geometrical and material properties.
Time-of-flight depth cameras are interesting sensors for contact-less 3D metrology because they combine mechanical robustness with independence of ambient lighting conditions. Their actual performance depends on many factors and is hard to predict from data sheets. In this study we investigate the deviations of the distance measurements of a high-end phasebased depth camera. We focus on the impact of (i) self-warming and external temperature, (ii) on range noise as a function of distance and acquisition time, and (iii) on distance-dependent biases. We present the dedicated experimental setups comprising a climate chamber, a calibration bench with a reference interferometer, and a laser tracker that provide controlled conditions and ground truth data. These setups allow investigating the absolute accuracy and mitigating repeatable distance biases by adapting the measurement model based on experimental data. For demonstration, we apply the investigation to two stateof-the-art industrial depth cameras of the same brand and type (Helios Lucid), showing significantly different response to external temperature but similar distance-dependent biases. We adapt the measurement model of one of the cameras for distancedependent inter-pixel biases and demonstrate that the resulting parameters reduce also the distance biases of the other camera by about 80 % to less than 1 mm at ranges of up to 1 m. This indicates the potential for batch error compensation. The paper contributes to better understanding distance deviations of depth cameras and to improving the accuracy of such cameras.
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.
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