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      Evaluation of the Azure Kinect and Its Comparison to Kinect V1 and Kinect V2

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          Abstract

          The Azure Kinect is the successor of Kinect v1 and Kinect v2. In this paper we perform brief data analysis and comparison of all Kinect versions with focus on precision (repeatability) and various aspects of noise of these three sensors. Then we thoroughly evaluate the new Azure Kinect; namely its warm-up time, precision (and sources of its variability), accuracy (thoroughly, using a robotic arm), reflectivity (using 18 different materials), and the multipath and flying pixel phenomenon. Furthermore, we validate its performance in both indoor and outdoor environments, including direct and indirect sun conditions. We conclude with a discussion on its improvements in the context of the evolution of the Kinect sensor. It was shown that it is crucial to choose well designed experiments to measure accuracy, since the RGB and depth camera are not aligned. Our measurements confirm the officially stated values, namely standard deviation ≤17 mm, and distance error <11 mm in up to 3.5 m distance from the sensor in all four supported modes. The device, however, has to be warmed up for at least 40–50 min to give stable results. Due to the time-of-flight technology, the Azure Kinect cannot be reliably used in direct sunlight. Therefore, it is convenient mostly for indoor applications.

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          RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments

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            Robust Part-Based Hand Gesture Recognition Using Kinect Sensor

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              Kinect range sensing: Structured-light versus Time-of-Flight Kinect

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                08 January 2021
                January 2021
                : 21
                : 2
                : 413
                Affiliations
                Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology STU in Bratislava, Ilkovičova 3, 812 19 Bratislava, Slovakia; martin.dekan@ 123456stuba.sk (M.D.); lubos.chovanec@ 123456stuba.sk (Ľ.C.); peter.hubinsky@ 123456stuba.sk (P.H.)
                Author notes
                Author information
                https://orcid.org/0000-0003-1697-1197
                https://orcid.org/0000-0003-2372-7467
                https://orcid.org/0000-0003-2917-6950
                Article
                sensors-21-00413
                10.3390/s21020413
                7827245
                33430149
                3906d08e-3563-4712-937d-ae13aa12ff9a
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 27 October 2020
                : 04 January 2021
                Categories
                Technical Note

                Biomedical engineering
                kinect,azure kinect,robotics,mapping,slam (simultaneous localization and mapping),hri (human–robot interaction),3d scanning,depth imaging,object recognition,gesture recognition

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