Abstract
RGB-D cameras are employed in several research fields and application scenarios. Choosing the most appropriate sensor has been made more difficult by the increasing offer of available products. Due to the novelty of RGB-D technologies, there was a lack of tools to measure and compare performances of this type of sensor from a metrological perspective. The recent ISO 10360-13:2021 represents the most advanced international standard regulating metrological characterization of coordinate measuring systems. Part 13, specifically, considers 3D optical sensors. This paper applies the methodology of ISO 10360-13 for the characterization and comparison of three RGB-D cameras produced by Intel® RealSense™ (D415, D455, L515) in the close range (100–1500 mm). ISO 10360-13 procedures, which focus on metrological performances, are integrated with additional tests to evaluate systematic errors (acquisition of flat objects, 3D reconstruction of objects). The present paper proposes an off-the-shelf comparison which considers the performance of the sensors throughout their acquisition volume. Results have exposed the strengths and weaknesses of each device. The D415 device showed better reconstruction quality on tests strictly related to the short range. The L515 device performed better on systematic depth errors; finally, the D455 device achieved better results on tests related to the standard.
Highlights
Once used only in applications that required high frame rates, depth cameras can nowadays be considered as a budget-option 3D optical coordinate measurement system
All of the results listed were obtained by acquiring 3D data for each test using the Depth Quality Tool software provided by Intel RealSense, subsequently processed using Geomagic Design X® 3D modeling software [30]
With regard to the 3D reconstruction test, in order to create the three-dimensional model of each acquired object, after removing 8% of the edges from each acquisition, it was necessary to perform an alignment process consisting of two phases, a first coarse alignment based on the manual selection of corresponding points and a fine alignment phase using the Iterative Closest Point (ICP) algorithm
Summary
Once used only in applications that required high frame rates, depth cameras can nowadays be considered as a budget-option 3D optical coordinate measurement system. Compact dimensions, low costs (w.r.t other professional 3D scanning systems), portability and easiness of use—i.e., the main features of such systems—have made RGB-D sensors the hardware of choice in several research and application fields. This study refers to low-budget compact sensors, typically identified with the alternative name of “depth cameras”. Pioneer products of this class of devices, such as the Microsoft Kinect [1], first commercialized in 2010, were essentially used as motion sensing and 3D tracking devices spreading in the fields of gaming and entertainment applications. Portability and compactness are distinctive features for the entire class of devices, different hardware is used as sensor, resulting in different operating principles. Two main technologies can be identified: (i) Stereoscopic systems; (ii) Time-of-Flight (ToF) systems
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