AUTHORS: Vladimir Tadic, Akos Odry, Istvan Kecskes, Ervin Burkus, Zoltan Kiraly, Peter Odry

Download as PDF

ABSTRACT: This paper presents the applications of depth cameras in robotics. The aim is to test the capabilities of depth cameras in order to better detect objects in images based on depth information. In the paper, the Intel RealSense depth cameras are introduced briefly and their working principle and characteristics are explained. The use of depth cameras in the example of painting robots is shown in brief. The utilization of the RealSense depth camera is a very important step in robotic applications, since it is the initial step in a series of robotic operations, where the goal is to detect and extract an obstacle on a wall that is not intended for painting. A series of experiments confirmed that camera D415 provides much more precise and accurate depth information than camera D435.

KEYWORDS: Depth image; measuring depth; RealSense cameras; image processing; obstacle detection

REFERENCES:

[ 1] Monica Carfagni, Rocco Furferi, Lapo Governi, Chiara Santarelli, Michaela Servi, “Metrological and Critical Characterization of the Intel D415 Stereo Depth Camera”, Sensors, 2019, 19, 489; doi:10.3390/s19030489

[2] Jia Hu, Yifeng Niu, Zhichao Wang, “Obstacle Avoidance Methods for Rotor UAVs Using RealSense”, 2017 Chinese Automation Congress (CAC), DOI: 10.1109/CAC.2017.8244068

[3] Silvio Giancola, Matteo Valenti, Remo Sala, ”A Survey on 3D Cameras: Metrological Comparison of Time-of-Flight, StructuredLight and Active Stereoscopy Technologies”, SpringerBriefs in Computer Science, Springer, ISSN 2191-5768, 2018, doi.org/10.1007/978-3- 319-91761-0

[4] Leonid Keselman, John Iselin Woodfill, Anders Grunnet-Jepsen, Achintya Bhowmik, “Intel RealSense Stereoscopic Depth Cameras”, arXiv:1705.05548

[5] Reginald L. Lagendijk, Ruggero E.H. Franich, Emile A. Hendriks, “Stereoscopic Image Processing”, The work was supported in part by the European Union under the RACE-II project DISTIMA and the ACTS project PANORAMA.

[6] Francesco Luke Siena, Bill Byrom, Paul Watts, Philip Breedon, “Utilising the Intel RealSense Camera for Measuring Health Outcomes in Clinical Research”, Journal of Medical Systems (2018) 42: 53, doi.org/10.1007/s10916-018- 0905-x

[7] “Intel RealSense D400 Series Product Family Datasheet”, New Technologies Group, Intel Corporation, 2019, Document Number: 337029-005

[8] Anders Grunnet-Jepsen, Dave Tong, “Depth Post-Processing for Intel® RealSense™ D400 Depth Cameras”, New Technologies Group, Intel Corporation, 2018, Rev 1.0.2

[9] “Evaluating Intel’s RealSense SDK 2.0 for 3D Computer Vision Using the RealSense D415/D435 Depth Cameras”, 2018, Berkeley Design Technology, Inc.

[10] “Intel® RealSense™ Camera Depth Testing Methodology”, New Technologies Group, Intel Corporation, 2018, Revision 1.0

[11] Anders Grunnet-Jepsen, John N. Sweetser, John Woodfill, “ Best-Known-Methods for Tuning Intel® RealSense™ D400 Depth Cameras for Best Performance”, New Technologies Group, Intel Corporation, Rev 1.9

[12] Anders Grunnet-Jepsen, Paul Winer, Aki Takagi, John Sweetser, Kevin Zhao, Tri Khuong, Dan Nie, John Woodfill, “Using the Intel® RealSenseTM Depth cameras D4xx in Multi-Camera Configurations”, New Technologies Group, Intel Corporation, Rev 1.1

[13] “Intel RealSense Depth Module D400 Series Custom Calibration”, New Technologies Group, Intel Corporation, 2019, Revision 1.5.0

[14] Anders Grunnet-Jepsen, John N. Sweetser, “Intel RealSens Depth Cameras for Mobile Phones”, New Technologies Group, Intel Corporation, 2019

[15] Philip Krejov, Anders Grunnet-Jepsen, “Intel RealSense Depth Camera over Ethernet”, New Technologies Group, Intel Corporation, 2019

[16] Joao Cunha and Eurico Pedrosa and Cristovao Cruz and Antonio J.R. Neves and Nuno Lau, “Using a Depth Camera for Indoor Robot Localization and Navigation”, Conference: RGB-D: Advanced Reasoning with Depth Cameras Workshop, Robotics Science and Systems onference (RSS), At LA, USA, 2011

[17] Hani Javan Hemmat, Egor Bondarev, Peter H.N. de With, “Real-time planar segmentation of depth images: from 3D edges to segmented planes”, Eindhoven University of Technology, Department of Electrical Engineering, Eindhoven, The Netherlands

[18] Fabrizio Flacco, Torsten Kroger, Alessandro De Luca, Oussama Khatib, “A Depth Space Approach to Human-Robot Collision Avoidance”, 2012 IEEE International Conference on Robotics and Automation RiverCentre, Saint Paul, Minnesota, USA May 14-18, 2012

[19] Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng, “3-D Depth Reconstruction from a Single Still Image”, International Journal of Computer Vision, 2008, Volume 76, Issue 1, pp 53–69

[20] Vladimiros Sterzentsenko, Antonis Karakottas, Alexandros Papachristou, Nikolaos Zioulis, Alexandros Doumanoglou, Dimitrios Zarpalas, Petros Daras, “A low-cost, flexible and portable volumetric capturing system”, Website: vcl.iti.gr

[21] Nicole Carey, Radhika Nagpal, Justin Werfel, “Fast, accurate, small-scale 3D scene capture using a low-cost depth sensor”, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), DOI: 10.1109/WACV.2017.146