We present an algorithm to distinguish several types of brick walls, picket fences, and hedges based on the analysis of backscattered sonar echoes. The echo data are acquired by a mobile robot with a single-transducer 50 kHz pulse-echo sonar computer-controlled scanning system packaged as its sensor head. For several locations along a wall, fence, or hedge, a fan of backscatter sonar echoes is acquired, digitized, and stored on a computer as the sonar transducer is swept over a horizontal arc. Off-line, each digitized echo waveform is then rectified, smoothed, and summed to give a measure of the backscatter strength. This backscatter is then plotted versus scan angle, with a series of N-peak deformable templates fit to these data for each scan. The number of peaks in the best-fitting N-peak template indicates the presence and location of retro-reflectors, and allows automatic categorization of the various fences, hedges, and brick walls.
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References
1.
Au, W. W. L. 1993. The Sonar of Dolphins, Springer-Verlag, Berlin.
2.
Ayrulu, B. and Barshan, B. 1998. Identification of target primitives with multiple decision-making sonars using evidential reasoning. International Journal of Robotics Research17(6):598-623.
3.
Ayrulu, B. and Barshan, B. 2001. Neural networks for improved target differentiation and localization with sonar. Neural Networks14(3):355-373.
4.
Barshan, B. and Ayrulu, B. 2002. Fractional Fourier transform preprocessing for neural networks and its application to object recognition. Neural Networks15(1):131-140.
5.
Barshan, B. and Ayrulu, B. 2003. Comparative analysis of different approaches to target differentiation and localization with sonar. Pattern Recognition36(5):1213-1231.
6.
Barshan, B. and Ayrulu, B. 2004. Fuzzy clustering and enumeration of target type based on sonar returns. Pattern Recognition37(2):189-199.
7.
Barshan, B. and Baskent, D. 2000. Comparison of two methods of surface profile extraction from multiple ultrasonic range measurements. Measurement Science and Technology11(6):833-844.
8.
Barshan, B. and Baskent, D. 2001a. Morphological surface profile extraction with multiple range sensors. Pattern Recognition34(7):1459-1467.
9.
Barshan, B. and Baskent, D. 2001b. Map building from range data with mathematical morphology. Active Sensors for Local Planning in Mobile Robotics, P. Probert Smith, editor, Series in Robotics and Intelligent Systems, World Scientific, Singapore, Vol. 26, Chapter 7, pp.111-135.
10.
Barshan, B. and Kuc, R. 1992. A bat-like sonar system for obstacle localization. IEEE Transactions on Systems, Man, and Cybernetics22(4):636-646.
11.
Barshan, B. and Sekmen, A. S. 1999. Radius of curvature estimation and localization of targets using multiple sonar sensors. Journal of the Acoustical Society of America105(4):2318-2331.
12.
Baskent, D. and Barshan, B. 1999. Surface profile determination from multiple sonar data using morphological processing. International Journal of Robotics Research18(8):788-808.
13.
Carelli, R. and Freire, E. O. 2003. Corridor navigation and wall-following stable control for sonar-based mobile robots. Robotics and Autonomous Systems45(3-4):235-247.
14.
Choi, M. S. and Kim, W.Y. 2002. A novel two stage template matching method for rotation and illumination invariance. Pattern Recognition35:119-129.
15.
Chong, K. S. and Kleeman, L. 1999a. Feature-based mapping in real, large-scale environments using an ultrasonic array. International Journal Robotics Research18(1):3-19.
16.
Chong, K. S. and Kleeman, L. 1999b. Mobile robot map building for an advanced sonar array and accurate odometry. International Journal of Robotics Research18(1):20-36.
17.
Chou, T. N. and Wykes, C. 1999. An integrated ultrasonic system for detection, recognition and measurement. Measurement26(3):179-190.
18.
Crowley, J. L. 1985. Navigation for an intelligent mobile robot. IEEE Journal of Robotics and Automation1(1):31-41.
19.
Deng, J.Y. and Lai, F. 1997. Region-based template deformation and masking for eye-feature extraction and description. Pattern Recognition30(3):403-419.
20.
Dror, I. E., Zagaeski, M., and Moss, C. F. 1995. Threedimensional target recognition via sonar: a neural network model. Neural Networks8(1):149-160.
21.
Flynn, A. M. 1988. Combining sonar and infrared sensors for mobile robot navigation. International Journal of Robotics Research7(6):5-13.
22.
Gao, W. 2005. Sonar sensor interpretation for ectogenous robots, Ph. D. Dissertation, College of William and Mary, Williamsburg, VA.
23.
Griffin, D. R. 1958. Listening in the Dark, Yale University Press, New Haven, CT.
24.
Harper, N. and McKerrow, P. 2001. Recognizing plants with ultrasonic sensing for mobile robot navigation. Robotics and Autonomous Systems34:71-82.
25.
Jain, A. K. and Vailaya, A. 1998. Shape-based retrieval: a case study with trademark image databases. Pattern Recognition31(9):1369-1390.
26.
Jain, A. K., Zhong, Y., and Lakshmanan, S. 1996. Object matching using deformable templates. IEEE Transactions on Pattern Analysis and Machine Intelligence18(3):267-278.
27.
Kleeman, L. 1999a. Real time mobile robot sonar with interference rejection. Sensor Review19(3):214-221.
28.
Kleeman, L. 1999b. Fast and accurate sonar trackers using double pulse coding. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Kyongju, Korea, October, pp.1185-1190.
29.
Kleeman, L. 2004. Advanced sonar with velocity compensation. International Journal of Robotics Research23(2):111-126.
30.
Kleeman, L. and Kuc, R. 1995. Mobile robot sonar for target localization and classification. International Journal of Robotics Research14(4):295-318.
31.
Kober, R. and Schnitzler, H.-U. 1990. Information in sonar echoes of fluttering insects available for echolocating bats. Journal of the Acoustical Society of America87(2):882-896.
32.
Krishna, K. M. and Kalra, P. K. 2002. Detection, tracking and avoidance of multiple dynamic objects. Journal of Intelligent and Robotic Systems33:371-408.
33.
Kuc, R. 1993. Three-dimensional tracking using qualitative bionic sonar. Robotics and Autonomous Systems11:213-219.
34.
Kuc, R. 1994. Sensorimotor model of bat echolocation and prey capture. Journal of the Acoustical Society of America96(4):1966-1977.
35.
Kuc, R. 1996. Biologically motivated adaptive sonar system. Journal of the Acoustical Society of America100(3):1849-1854.
36.
Kuc, R. 1997. Biomimetic sonar recognizes objects using binaural information. Journal of the Acoustical Society of America102(2):689-696.
37.
Kuc, R. 2001. Biomimetic sonar recognizes objects from echoes. Biosonar, J. Thomas, editor, University of Chicago Press, Chicago.
38.
Kuc, R. 2003. Recognizing retro-reflectors with an obliquely oriented multipoint sonar and acoustic flow. International Journal of Robotics Research22(2):129-148.
39.
Kuc, R. 2004. Neuro-computational processing of moving sonar echoes classifies and localizes foliage. Journal of the Acoustical Society of America116(3):1811-1818.
40.
Lane, D. M. and Stoner, J. P. 1994. Automatic interpretation of sonar imagery using qualitative feature matching. IEEE Journal of Oceanic Engineering19(3):391-405.
41.
Leonard, J. J. and Durrant-Whyte, H. F. 1992. Directed Sonar Sensing for Mobile Robot Navigation, Kluwer Academic, Dordrecht.
42.
Llata, J. R., Sarabia, E. G., and Oria, J. P. 2002. Threedimensional robotic vision using ultrasonic sensors. Journal of Intelligent and Robotics Systems33:267-284.
43.
McKerrow, P. J. and Harper, N. L. 1999. Recognizing leafy plants with in-air sonar. Sensor Review19(3):202-206.
44.
McKerrow, P. and Harper, N. 2001. Plant acoustic density profile model of CTFM ultrasonic sensing. IEEE Sensors Journal1(4):245-255.
45.
Maxim, H. 1912. Preventing collisions at sea: a mechanical application of the bat’s sixth sense. Scientific American27:80-81.
46.
Mignotte, M., Collet, C., Perez, P., and Bouthemy, P. 2000. Hybrid genetic optimization and statistical model-based approach for the classification of shadow shapes in sonar imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence22(2):129-141.
47.
Probert Smith, P. and Zografos, K. 2005. Sonar for recognizing the texture of pathways. Robotics and Autonomous Systems51:17-28.
48.
Ratner, D. and McKerrow, P. 2003. Navigating an outdoor robot along continuous landmarks with ultrasonic sensing. Robotics and Autonomous Systems45:73-82.
49.
Rosefeld, A. and Kak, A. C. 1982. Digital Picture Processing, 2nd edition, Academic Press, New York, Vol. 2, Chapter 9.
50.
Sasaki, K. and Takano, M. 1992. Classification of objects’ surface by acoustic transfer function. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Raleigh, NC, July 7-10, pp. 821-828.
51.
Sekmen, A. S. and Barshan, B. 2001. Estimation of object location and radius of curvature using ultrasonic sonar. Applied Acoustics62(7):841-865.
52.
Simmons, J. A. 1989. The acoustic basis for target discrimination by FM echolocating bats. Journal of the Acoustical Society of America86(4):1333-1350.
53.
Simmons, J. A., Brock Fenton, M., and O’Farrell, M. J. 1979. Echolocation and pursuit of prey by bats. Science203:16-21.
54.
Uenohara, M. and Kanade, T. 1997. Use of Fourier and Karhunen-Loeve decomposition for fast pattern matching with a large set of templates. IEEE Transactions on Pattern Analysis and Machine Intelligence19(8):891-898.
55.
Ullate, L. G., Sanchez, M. T., Villanueva, E., Parrilla, M., and Anaya, J. J. 1993. A three-transducer ultrasonic system for object location in air. Sensors and Actuators A37-38:391-396.
56.
Utete, S. W., Barshan, B., and Ayrulu, B. 1999. Voting as validation in robot programming. International Journal of Robotics Research18(4):401-413.
57.
Yuille, A. L., Hallinan, P. W., and Cohen, D. S. 1992. Feature extraction from faces using deformable templates. International Journal of Computer Vision8(2):99-111.
