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RGB-D Sensors in Social Robotics

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EN
Abstrakty
EN
This article presents the results of a series of experiments carried out using a Kinect for Windows sensor coupled with dedicated software. The focus of this study is on the use of such devices in the field of social robotics. Two software packages are considered - Microsoft Kinect SDK and OpenNI coupled with NiTE library. Particular emphasis is placed on the parameters affecting the social competencies of a robot, such as the speed of detecting users, the accuracy of establishing position and orientation of a user or stability of the tracking process. Key characteris tics of the evaluated software packages are identified and differences regarding their usage outlined in view of interaction oriented algorithms.
Twórcy
autor
  • Chair of Cybernetics and Robotics, Wrocław University of Technology, Wybrzeże Wyspiań skiego 27, 50-370 Wrocław,
autor
  • Chair of Cybernetics and Robotics, Wrocław University of Technology, Wybrzeże Wyspiań skiego 27, 50-370 Wrocław,
  • Chair of Cybernetics and Robotics, Wrocław University of Technology, Wybrzeże Wyspiań skiego 27, 50-370 Wrocław, www: http://lirec.iiar.pwr.edu.pl/~jkedzier
Bibliografia
  • [1] R. A. Clark and et al., “Validity of the Microsoft Kinect for assessment of postural control”, Gait and Posture, vol. 36, no. 3, 2012, 372–377, DOI:10.1016/j.gaitpost.2012.03.033.
  • [2] FLASH. “Homepage”. http://flash.iiar.pwr.edu.pl, 2014.
  • [3] H. Gonzalez-Jorge, B. Riveiro, E. Vazquez-Fernandez, J. Martı́nez-Sánchez, and P. Arias, “Metrological evaluation of Microsoft Kinect and Asus Xtion sensors”, Measurement, vol. 46, 2013, 1800–1806,DOI:10.1016/j.measurement.2013.01.011.
  • [4] P. Henry and et al., “RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments”, International Journal of Robotics Research, vol. 31, no. 5, 2012,647–663, DOI:10.1177/0278364911434148.
  • [5] T. Huynh, R. Min, and J. Dugelay, “An Efficient LBP-Based Descriptor for Facial Depth Images Applied to Gender Recognition Using RGB-D Face Data”, Lecture Notes in Computer Science, vol. 7728, 2013, 133–145, DOI:10.1007/978-3-642-37410-4_12.
  • [6] J. Kędzierski. System sterowania robota społecznego. PhD thesis, Politechnika Wrocławska, Wrocław, 2014.
  • [7] K. Khoshelham, “Accuracy Analysis of Kinect Depth Data”, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVIII-5/W12, 2011, 133–138, DOI:10.5194/isprsarchives-XXXVIII-5-W12-133-2011.
  • [8] K. Khoshelham and S. O. Elberink, “Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications”, Sensors, vol. 12, 2012, 1437–1454, DOI:10.3390/s120201437.
  • [9] Microsoft. “Kinect SDK documentation”.http://msdn.microsoft.com/en-us/ library/hh855347.aspx, 2014.
  • [10] I. Oikonomidis, N. Kyriazis, and A. Argyros, “Efficient model-based 3D tracking of hand articulations using Kinect”. In: Proceedings of the British Machine Vision Conference, 2011, 101.1–101.11,DOI:10.5244/C.25.101.
  • [11] OpenNI. “Project website”.http://www.openni.org/, 2014.
  • [12] A. Ramey, V. Gonzalez-Pacheco, and M. A. Salichs,“Integration of a low-cost RGB-D sensor in a social robot for gesture recognition”. In: 2011 6th ACM/IEEE International Conference on Human-Robot Interaction, 2011, 229–230, DOI:10.1145/1957656.1957745.
  • [13] UKinect. “Module documentation”.http://lirec.ict.pwr.wroc.pl/ 􀏐lash/?q=node/110,2014.
  • [14] UKinectOpenNI2. “Module documentation”.http://lirec.ict.pwr.wroc.pl/ flash/?q=node/121,2014.
  • [15] Urbi. “Project website”.http://www.urbiforge.org, 2014.
  • [16] D. Vasquez and et al., “Human Aware Navigation for Assistive Robotics”, Experimental Robotics, vol. 88, 2013, 449–462, DOI:10.1007/978-3-319-00065-7_31.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5476e84f-57a8-4058-9a26-ffd97f773dae
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