Modeling and classification of rough surfaces using CTFM sonar imaging

The typical use of ultrasonic sensors has been limited to estimation of the location of targets in a robot workspace. CTFM sonars have also been used successfully in classifying primitive targets. In this paper the classification is extended to include textures typical of these found in pathways the robot may need to follow or identify. The pathway classes examined are considered to be plane surfaces of various roughness corresponding to hard smooth floor, carpet, and asphalt. Each class is modeled using an extension of the Kirchhoff approximation method describing the scattering of the acoustic wave on rough surfaces. The CTFM sonar image corresponding to each class is derived and compared with the experimental one. Then a feature is extracted that exploits the differences between the three surface models. A neural network is trained for recognition with excellent results.