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

Bogdan Kreczmer

doi:10.1515/amcs-2016-0008

.blurhash-client-img { display: none !important; }

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

International Journal of Applied Mathematics and Computer Science

Volume 26 (2016): Issue 1 (March 2016)

By: Bogdan Kreczmer

Open Access

|Mar 2016

Abstract

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.

References

Adib, F. and Katabi, D. (2013). See through wall with Wi-Fi, ACM SIGCOMM’13, Hong Kong, China, pp. 75–86.
Search in Google Scholar
Barshan, B. (1999). Location and curvature estimation of spherical targets using multiple sonar time-of-flight measurements, IEEE Transactions on Instrumentation and Measurement48(6): 1212–1223.10.1109/19.816139
Search in Google Scholar
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 Intelligence12(6): 560 – 569.10.1109/34.56192
Search in Google Scholar
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 Intelligence13(12): 1260–1269.10.1109/34.107000
Search in Google Scholar
Brown, M.K. (1985). Feature extraction techniques for recognizing solid objects with an ultrasonic range sensor, IEEE Journal of Robotics and Automation1(4): 191–205.10.1109/JRA.1985.1087022
Search in Google Scholar
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.
Search in Google Scholar
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 Control60(2): 343–355.10.1109/TUFFC.2013.257023357908
Search in Google Scholar
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.
Search in Google Scholar
Kleeman, L. (2004). Advanced sonar with velocity compensation, International Journal of Robotics Research23(2): 111–126.10.1177/0278364904041321
Search in Google Scholar
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.
Search in Google Scholar
Kleeman, L. and Kuc, R. (1995). Mobile robot sonar for target localization and classification, International Journal of Robotics Research14(4): 295–318.10.1177/027836499501400401
Search in Google Scholar
Kreczmer, B. (2010). Objects localization and differentiation using ultrasonic sensors, in H. Yussof (Ed.), Robot Localization and Map Building, InTech, Rijeka, pp. 521–543.10.5772/9271
Search in Google Scholar
Kreczmer, B. (2013). Relations between classification criteria of objects recognizable by ultrasonic systems, 16th IEEE International Conference MMAR 2011, Międzyzdroje, Poland, pp. 806–811.
Search in Google Scholar
Kuc, R. and Siegel, M. (1987). Physically based simulation model for acoustic sensor robot navigation, IEEE Transactions on Pattern Analysis and Machine IntelligencePAMI-9(6): 766–778.10.1109/TPAMI.1987.4767983
Search in Google Scholar
Leonard, J.J. and Durrant-Whyte, H.F. (1991). Mobile robot localization by tracking geometric beacons, IEEE Transactions on Robotics and Automation7(3): 376–382.10.1109/70.88147
Search in Google Scholar
Leonard, J.J. and Durrant-Whyte, H.F. (1992). Directed Sonar Sensing for Mobile Robot Navigation, Kluwer Academic Publishers, Boston, MA/London/Dordrecht.10.1007/978-1-4615-3652-9
Search in Google Scholar
Möller, M.M. (1995). Autonomous mobility with triaural sonar system, International Symposium on Intelligent Robotic Systems, Pisa, Italy, pp. 25–30.
Search in Google Scholar
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 Applications79(7): 15–18.10.5120/13753-1589
Search in Google Scholar
Peremans, H., Audenaert, K. and Campenhout, J.M.V. (1993). A high-resolution sensor based on tri-aural perception, IEEE Transactions on Robotics and Automation9(1): 36–48.10.1109/70.210793
Search in Google Scholar
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.
Search in Google Scholar
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 Measurement59(12): 1–10.10.1109/TIM.2010.2047305
Search in Google Scholar
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.
Search in Google Scholar