Abstract
Low-cost RGB-D cameras are increasingly being used in several research fields, including human–machine interaction, safety, robotics, biomedical engineering and even reverse engineering applications. Among the plethora of commercial devices, the Intel RealSense cameras have proven to be among the most suitable devices, providing a good compromise between cost, ease of use, compactness and precision. Released on the market in January 2018, the new Intel model RealSense D415 has a wide acquisition range (i.e., ~160–10,000 mm) and a narrow field of view to capture objects in rapid motion. Given the unexplored potential of this new device, especially when used as a 3D scanner, the present work aims to characterize and to provide metrological considerations for the RealSense D415. In particular, tests are carried out to assess the device performance in the near range (i.e., 100–1000 mm). Characterization is performed by integrating the guidelines of the existing standard (i.e., the German VDI/VDE 2634 Part 2) with a number of literature-based strategies. Performance analysis is finally compared against the latest close-range sensors, thus providing a useful guidance for researchers and practitioners aiming to use RGB-D cameras in reverse engineering applications.
Highlights
Introduction and BackgroundThree-dimensional optical systems have found popularity in numerous fields of application spanning from robotics [1], automotive [2], industrial [3], mechanical engineering and cultural heritage [4,5], to the biomedical field [6,7,8]
The results shown here can be compared with the results obtained by scanning the same objects with the SR300 (Table 7)
Given the growing popularity of RGB-D devices mainly due to their versatility and the interest that Intel RealSense devices have obtained in recent years for their accuracy, compactness and ease of use, in this work, a metrological evaluation of the latest model presented by Intel, the RealSense
Summary
Three-dimensional optical systems have found popularity in numerous fields of application spanning from robotics [1], automotive [2], industrial [3], mechanical engineering and cultural heritage [4,5], to the biomedical field [6,7,8]. To overcome previous camera releases and strengthen their leading position in the market, Intel launched two new depth cameras in January 2018: the D415 and the D435 models Such devices differ from each other mainly in the field of view (FOV) angles and in the exposition time of the camera-integrated shutter. Giancola et al [33] proposed characterizations for the Microsoft Kinect v2, the Orbbec Astra S and the Intel D400 series For each of these devices, two types of experiments were performed, one for pixel-wise and one for sensor-wise characterization to evaluate, respectively, the accuracy of the cameras at different distances and the quality of the reconstruction of known geometries.
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