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

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

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

4. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 4 Ultrasonic Signal Introducction  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. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 5 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 the amplitude 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. Introducction

6. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 6 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 are previous to the main peak from the object to be classified. Introducction The Classification Problem (Wall/Corner)

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

8. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 8  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. (1) Amplitude Model

9. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 9 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 = 2corner target (1) Amplitude Model

10. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 10 How to apply the amplitude model to classification problem: An entire circular scan from the scene is performed, and the peak amplitude values of echoes are obtained (  =0º) In order to classify each located peak only one parameter of the model is necessary: the value of the Cr of the surface. — x it is the distance to the object, obtained from the echo (ToF) —  it is not necessary, since the ‘mountain peak’ always correspond with =0º —  and A 0 are constants well-known at the calibration stage. Amplitude Model

11. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 11 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 = 2corner target Amplitude Model (2) How to apply the amplitude model to classification problem (II):

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

13. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 13 Geometric Features Differences in the echoes from corners and walls

14. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 14 Geometric Features Differences in the echoes from corners and walls

15. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 15 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:  The distance to the corner, dc, will agree with : Geometric Features (3) (4) Differences in the echoes from corners and walls

16. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 16  The amplitude of the ghost peaks can be also predicted using the amplitude model. Geometric Features Differences in the echoes from corners and walls

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

18. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 18  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) Classification Algorithms Classification Algorithms

19. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 19 A.C.A. (Amplitude Classification Algorithm) A.C.A. (Amplitude Classification Algorithm) Example of normal distributions of parameter N for Walls and Corners Membership probability of Walls(blue) and Corners(red) Corners Walls Classification Algorithms  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 :  If P(W) > P(C) then Detected Object is a Wall, else a Corner P(C) P(W)

20. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 20  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 p i = (x 1,x 2,x 3,....,x n )  Given an object to classify o i = (x 1,x 2,x 3,....,x n ) the algorithm is as follows: T he 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. K-nearest neighbours method (k-nn) Classification Algorithms

21. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 21 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 the Corners are used.  The value of the parameter k is 10 this value demonstrated to be a good compromise aw 1 and aw 2 are real amplitudes Aw 1 and Aw 2 are theorethical amplitudes from eq. (1) Nis obtained from the eq. (2)  1 and  2 are angles calculated from eq. (3) Classification Algorithms K-nearest neighbours method (k-nn)

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

23. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 23  Four data sets have been used for the experiments with the two classification algorithms : MaterialsOrientations Distances(m) Walls Corners 1 Cement, Pladur 20º a 70º0.5 a 4 1279 650 2 Cement, Pladur 20º a 70º0.5 a 4 855 309 3 Cement 20º a 70º0.5 a 4 650 321 4 Cement, Melamine 20º a 70º0.5 a 4 600 354 Experimental Results in classification Experimental Results

24. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 24 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% A.C.A. Algorithm (Cr =0.6, N0 = 1.5) K-nn Algorithm (k = 10) Experimental Results Experimental Results in classification

25. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms Conclusions 25 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% A.C.A. Algorithm (Cr =0.6, N0 = 1.5) K-nn Algorithm (k = 10) Experimental Results Experimental Results in classification

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

27. Experimental Results Introducction Amplitude Model Geometric Features Classification Algorithms 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: Ø Taking into account that the measurements were exclusively data taken from only one scan and from only one position, as well as the distances up to 4m, and Ø k-nn algorithm yields the best results in all the situations, but its higher computational cost must also be considered when real time response is required.

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