1 / 28

Wall-Corner Classification. A New Ultrasonic Amplitude Based Approach

Wall-Corner Classification. A New Ultrasonic Amplitude Based Approach. M. Martínez, G. Benet, F.Blanes, P. Pérez , J.E. Simó, J.L.Poza.

joie
Download Presentation

Wall-Corner Classification. A New Ultrasonic Amplitude Based Approach

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Wall-Corner Classification. A New Ultrasonic Amplitude Based Approach M. Martínez, G. Benet, F.Blanes, P. Pérez, J.E. Simó, J.L.Poza Universidad Politécnica de ValenciaDep. Informática de Sistemas y ComputadoresCamino de Vera s/n Valencia (SPAIN){mimar, gbenet , pblanes, pperez, jsimo, jopolu @disca.upv.es} Lisboa, 6 July 2004

  2. Contens • Introducction • Amplitude Model • Geometric Features • Classification Algorithms • Experimentals Results • Conclusions 2

  3. Introducction Contens • Introducction • Amplitude Model • Geometric Features • Classification Algorithms • Experimental Results • Conclusions

  4. Introducction Ultrasonic Signal • Sonar sensing is one of the most useful and cost-effective methods of environment perception in autonomous robots. • Ultrasonic transducers are light, robust, and inexpensive devices. • The figure plots the envelope of a received echo. • Each peak is matched with a detected object.

  5. Introducction The Classification Problem (Wall/Corner) • In order to classify the targets most sonar systems employ only time of fly(ToF)information. • Planes are differentiated from corners by taking two measurements from two or more separate locations. • All these techniques have in common the extreme precision required in the ToF estimation. • Several authors have used theamplitude of the received echo to enhance their classification results. But, • The amplitude is a parameter very sensitive: • to environmental conditions, and • to the surface characteristics of each reflector • There is not a model of the amplitude reflected of an object.

  6. Introducction The Classification Problem (Wall/Corner) • The purpose of this paper is to present a method of classify between Walls and Corners, using: • Only one rotating ultrasonic sensor, which is composed of Piezo-ceramic ultrasonic sensors. • A simple but effective amplitude model, and • Information on “ghosts peaks”, which areprevious to the main peak from the object to be classified.

  7. Amplitude Model Contens • Introducction • Amplitude Model • Geometric Features • Classification Algorithms • Experimental Results • Conclusions

  8. Amplitude Model (1) • In a previous paper, a theoretical model for the amplitude of received ultrasonic echoes has been presented. • This model can be used to predict the expected amplitude of echoes from simple reflectors, like planes or right corners. • Also, this model can be used to classify the received echoes from bewteen two types of reflectors, following a statistical approach.

  9. Amplitude Model (1) Main parameters of the model: • Cr is the reflection coefficient. • It is a number between 0 and 1. • It represents the ratio between the intensity returned back to the transducer and the incident intensity of the acoustic beam. • N is a parameter which can take two values: • N = 1 wall target • N = 2 corner target

  10. Amplitude Model • Anentire circular scan from the scene is performed, and the peak amplitude values of echoes are obtained (=0º) • In order to classify each located peak onlyone parameter of the model isnecessary: the value of the Cr of the surface. • xit is the distance to the object, obtained from the echo (ToF) • it is not necessary, since the ‘mountain peak’ always correspond with =0º •  and A0are constants well-known at the calibration stage. How to apply the amplitude model to classification problem:

  11. Amplitude Model (2) How to apply the amplitude model to classification problem (II): Under these conditions, the value of N can be derived from equation (1): • The value of N obtained from this equation can be used for target classification purposes: • N = 1 wall target • N = 2 corner target

  12. Geometric Features Contens • Introducction • Amplitude Model • Geometric Features • Classification Algorithms • Experimental Results • Conclusions

  13. Geometric Features Differences in the echoes from corners and walls

  14. Geometric Features Differences in the echoes from corners and walls

  15. Geometric Features Differences in the echoes from corners and walls The ghost peaks previous to the corner’s main peak, must accomplish the following relationships: • The angles 1 and 2 are complementary, and will be calculated as follows: (3) • The distance to the corner, dc, will agree with : (4)

  16. Geometric Features Differences in the echoes from corners and walls • The amplitude of the ghost peaks can be also predicted using the amplitude model.

  17. Classification Algorithms Contens • Introducction • Amplitude Model • Geometric Features • Classification Algorithms • Experimental Results • Conclusions

  18. Classification Algorithms Classification Algorithms • By direct application of the amplitude model: • A.C.A. (for Amplitude Classification Algorithm) • By adaptation of a classic algorithm in the recognition of patterns: • K-nearest neighbours method (k-nn)

  19. Classification Algorithms A.C.A. (Amplitude Classification Algorithm) • The parameter N obtained from equation (1) has a quasi normal distribution around the value 1 in the case of walls, and 2 for corners: Corners Walls P(W) P(C) Example of normal distributions of parameter N for Walls and Corners Membership probability of Walls(blue) and Corners(red) • If P(W) > P(C) then Detected Object is a Wall, else a Corner

  20. Classification Algorithms K-nearest neighbours method (k-nn) • Classic algorithm in pattern recognition: • A training set of patterns for each class is used: Wall and Corner. • Each pattern is composed of one set of characteristics pi= (x1,x2,x3,....,xn) • Given an object to classifyoi= (x1,x2,x3,....,xn) the algorithm is asfollows: • The Euclidean distance to each pattern of each class is calculated. • The k patterns with smaller distances are choosed. • The obstacle will be classified into the class with more occurrences into the k set of patterns.

  21. Classification Algorithms K-nearest neighbours method (k-nn) Some details are given on the application of the algorithm: • The feature vector has four parameters: • A set of 400 patterns for the Wall class, and other 400 for theCornersare used. • The value of the parameter k is 10 • this value demonstrated to be a good compromise aw1 and aw2are real amplitudes Aw1 and Aw2 are theorethical amplitudes from eq. (1) N is obtained from the eq. (2) 1 and 2 are angles calculated from eq. (3)

  22. Experimental Results Contens • Introducction • Amplitude Model • Geometric Features • Classification Algorithms • Experimental Results • Conclusions

  23. Experimental Results Experimental Results in classification • Four data sets have been used for the experiments with the two classification algorithms: Materials Orientations Distances(m) Walls Corners 1 Cement, Pladur 20º a 70º 0.5 a 4 1279 650 2 Cement, Pladur 20º a 70º 0.5 a 4 855 309 3 Cement 20º a 70º 0.5 a 4 650 321 4 Cement, Melamine 20º a 70º 0.5 a 4 600 354

  24. Experimental Results Experimental Results in classification A.C.A. Algorithm (Cr =0.6, N0 = 1.5) K-nn Algorithm (k = 10) 1 2 3 4 1 2 3 4 Wall 88% 79% 92% 61% 88% 79% 92% 61% Corner 85% 83% 87% 19% 91% 91% 90% 84%

  25. Experimental Results Experimental Results in classification A.C.A. Algorithm (Cr =0.6, N0 = 1.5) K-nn Algorithm (k = 10) 1 2 3 4 1 2 3 4 Wall 88% 82% 90% 67% 88% 79% 92% 61% Corner 68% 46% 67% 13% 91% 91% 90% 84%

  26. Conclusions Contens • Introducction • Amplitude Model • Geometric Features • Classification Algorithms • Experimental Results • Conclusions

  27. Conclusions • In this work, a simple model of the amplitude response of the ultrasonic echoes has been used to classify between walls and corners. • The ultrasonic signal comes from a unique pair of rotating emitter/receiver transducers. • The amplitude of the echoes together with their time of flight(ToF) can be used in a simple data fusion process. • geometric features of the two main types of reflectors has been exploited. • The showed results yield very satisfactory success percentages: • Taking into account that the measurements were exclusively data taken from only one scan and from only one position, as well asthe distances up to 4m, and • k-nn algorithm yields thebest results in all the situations, but itshigher computational costmust also be considered when real time response is required.

  28. Wall-Corner Classification. A New Ultrasonic Amplitude Based Approach M. Martínez, G. Benet, F.Blanes, P. Pérez, J.E. Simó, J.L.Poza Universidad Politécnica de ValenciaDep. Informática de Sistemas y ComputadoresCamino de Vera s/n Valencia (SPAIN){mimar, gbenet , pblanes, pperez, jsimo, jopolu @disca.upv.es} Lisboa, 6 July 2004

More Related