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Connections between object classification criteria using an ultrasonic bi-sonar system

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents connections between the criteria which make three types of objects possible to be recognized, namely, edges, planes and corners. These criteria can be applied while a binaural sonar system is used. It is shown that the criteria are specific forms of a general equation. The form of the equation depends on a single coefficient. In the paper, the meaning of this coefficient is discussed. The constructions of the arrangement of objects are presented and are bound with values of the coefficient.
Rocznik
Strony
123--132
Opis fizyczny
Bibliogr. 22 poz., rys., wykr.
Twórcy
autor
  • Chair of Cybernetics and Robotics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-320 Wrocław, Poland
Bibliografia
  • [1] Adib, F. and Katabi, D. (2013). See through wall with Wi-Fi, ACM SIGCOMM’13, Hong Kong, China, pp. 75–86.
  • [2] Barshan, B. (1999). Location and curvature estimation of spherical targets using multiple sonar time-of-flight measurements, IEEE Transactions on Instrumentation and Measurement 48(6): 1212–1223.
  • [3] Barshan, B. and Kuc, R. (1990). Differentiating sonar reflections from corners and planes by employing an intelligent sensor, IEEE Transactions on Pattern Analysis and Machine Intelligence 12(6): 560–569.
  • [4] Bozma, O. and Kuc, R. (1991). Building a sonar map in a specular environment using a single mobile sensor, IEEE Transactions on Pattern Analysis and Machine Intelligence 13(12): 1260–1269.
  • [5] Brown, M.K. (1985). Feature extraction techniques for recognizing solid objects with an ultrasonic range sensor, IEEE Journal of Robotics and Automation 1(4): 191–205.
  • [6] Heale, A. and Kleeman, L. (2001). Fast target classification using sonar, 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, HI, USA, Vol. 3, pp. 1446–1451.
  • [7] Jackson, J.C., Summan, R., Dobie, G.I., Whiteley, S.M., Pierce, S.G. and Hayward, G. (2013). Time-of-flight measurement techniques for airborne ultrasonic ranging, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 60(2): 343–355.
  • [8] Kleeman, L. (2002). On-the-fly classifying sonar with accurate range and bearing estimation, IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland, Vol. 1, pp. 178–183.
  • [9] Kleeman, L. (2004). Advanced sonar with velocity compensation, International Journal of Robotics Research 23(2): 111–126.
  • [10] Kleeman, L. and Kuc, R. (1994). An optimal sonar array for target localization and classification, IEEE International Conference on Robotics and Automation, San Diego, CA, USA, pp. 3130–3135.
  • [11] Kleeman, L. and Kuc, R. (1995). Mobile robot sonar for target localization and classification, International Journal of Robotics Research 14(4): 295–318.
  • [12] Kreczmer, B. (2010). Objects localization and differentiation using ultrasonic sensors, in H. Yussof (Ed.), Robot Localization and Map Building, InTech, Rijeka, pp. 521–543.
  • [13] Kreczmer, B. (2013). Relations between classification criteria of objects recognizable by ultrasonic systems, 16th IEEE International Conference MMAR 2011, Międzyzdroje, Poland, pp. 806–811.
  • [14] Kuc, R. and Siegel, M. (1987). Physically based simulation model for acoustic sensor robot navigation, IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI- 9(6): 766–778.
  • [15] Leonard, J.J. and Durrant-Whyte, H.F. (1991). Mobile robot localization by tracking geometric beacons, IEEE Transactions on Robotics and Automation 7(3): 376–382.
  • [16] Leonard, J.J. and Durrant-Whyte, H.F. (1992). Directed Sonar Sensing for Mobile Robot Navigation, Kluwer Academic Publishers, Boston, MA/London/Dordrecht.
  • [17] Möller, M.M. (1995). Autonomous mobility with triaural sonar system, International Symposium on Intelligent Robotic Systems, Pisa, Italy, pp. 25–30.
  • [18] Nanani, G.K. and Prasad, K.M.V.V. (2013). A study of wi-fi based system for moving object detection through the wall, International Journal of Computer Applications 79(7): 15–18.
  • [19] Peremans, H., Audenaert, K. and Campenhout, J.M.V. (1993). A high-resolution sensor based on tri-aural perception, IEEE Transactions on Robotics and Automation 9(1): 36–48.
  • [20] Peremans, H., Campengout, J.V. and Levrouw, L. (1991). Steps towards tri-aural perception, in P.S. Schenker (Ed.), Sensor Fusion IV: Control Paradigms and Data Structures, SPIE, Bellingham, WA, pp. 165–176.
  • [21] Queirós, R., Corrêa Alegria, F., Silva Girão, P. and Cruz Serra, A. (2010). Cross-correlation and sine-fitting techniques for high-resolution ultrasonic ranging, IEEE Transactions on Instrumentation and Measurement 59(12): 1–10.
  • [22] Rencken, W.D., Peremans, H. and Möller, M. (1994). Tri-aural versus conventional sonar localisation and map building, JAS-4 Conference, Karlsruhe, Germany, pp. 398–402.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b9adbcb1-22f0-486d-95d7-4b6580a67230
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