Feature Extraction from a Broadband Sonar Sensor for Mapping Structured Environments Efficiently

Abstract

In this paper, we describe the use of a broadband, frequencymodulated sonar sensor (a CTFM sonar) for generating maps of structured environments for robots and autonomous systems. The major advantage of using these sensors is that the information generated is reliable enough to extract the geometry and type of certain features with very few measurements; hence, maps can be generated rapidly. Although the technology of low-cost CTFM sonar has been available for some time, it has only very recently been considered seriously as a candidate sensor for navigation and mapping in spite of its superior performance. We describe the operation of the sensor and the use of both the range-orientation and amplitude-orientation profiles to extract information online and point targets in both smooth and rough environments. We demonstrate the method through generating a map for a room typical of a modern building (in fact, in a domestic house), using resolution of scanning increment from 1.8 deg down to 10.8 deg. We examine the robustness of the method as the number of measurements is reduced through introducing two quality metrics for each map.

Automotive System for Remote Surface Classification

“…For the ultrasonic sensor we can express the amplitude of the received sonar signal in terms of these quantities [10]:$Afalse(rfalse)=A_{0}Gfalse(rfalse)Qfalse(\theta false){\rho }_b$ where $A_0$ is a constant, $Gfalse(rfalse)$ is the transmission gain function which attenuates with the distance to the surface r , $Qfalse(\theta false)$ is the directivity of the receiver and transmitter pair at grazing angle $\theta false(rfalse)$, and ${\rho }_b$ is the backscattering coefficient characterizing roughness and texture of the surface.…”

“…A surface can be characterized as random Gaussian process and described by three parameters $false({\rho }_S,T_S,{\sigma }_{S}false)$, where the standard deviation of surface heights is ${\sigma }_S$, the reflection coefficient is ${\rho }_S$, and the correlation length is $T_S$ [13]. For such a surface, the backscattering coefficient is given as [10]: $\langle \left|{\rho }_b\right|\rangle =\sqrt{\frac{\pi }{S}}\frac{\eta {\rho }_S}{2k_{air}{\cos }^3\theta }{e}^{-{\tan }^2\theta \cdot {\eta }^2/2}$ where S is the area of the reflecting surface, $\eta =T_S/{\sigma }_S$ is the roughness parameter, and ${\rho }_S$ is the equivalent reflection coefficient for scattering in the specular direction.…”

“…The objectives of the papers [9,10,11,12,13,14,15,16] were to classify surfaces using ultrasonic signals, primarily for use in robotics. In the case of robotic applications the task of classification is simplified comparing with automotive, as robots speed is usually low and the distance is limited to a few meters.…”

“…A broadband, frequency modulated sonar sensor was effectively used to extract information about the geometry and types of certain surfaces in [10]. In [11,12] a discrimination of different kinds of surfaces has been discussed.…”

Continuous Transmission Frequency Modulation Detection under Variable Sonar-Target Speed Conditions

“…Ultrasonic ranging sensors have been widely used in collision avoidance [1], navigation [2,3], mapping [4,5], target classification [6,11], and in travel aid devices for visually impaired individuals [7,8]. The ultrasonic ranging sensor, which is low cost, accurate in positioning, and easy to operate, is considered as a competitive choice for implementing target detection and recognition.…”