Modeling and classification of rough surfaces using CTFM sonar imaging

Politis, Z.; Probert, P.J.

doi:10.1109/robot.1999.774051

Cited by 11 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their measurements included all the energy in the echo (both specular and diffuse). Probert Smith and co-workers [6]- [8] also used a CTFM system transmitting at an angle to the surface. They used a set of three features: energy in the specular region, energy in the diffuse region, and range of bins in the diffuse region above a threshold.…”

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Classifying Surface Roughness With CTFM Ultrasonic Sensing

McKerrow

Kristiansen²

2006

IEEE Sensors J.

View full text Add to dashboard Cite

Roughness is a characteristic of a surface that is a function of its geometry. Ultrasonic sensing in air provides range, area, and angle information because the surface geometry determines the characteristics of the echo. The authors introduce the "spatial-angle-filter model" to explain the impact of surface roughness on the echo. On the basis of this model, they design a set of features for use in classifying surfaces. The quality of the features and the classification is measured with the Mahalanobis distance. The resultant system is able to achieve 99.73% classification of a set of 12 surfaces using five features. Abstract-Roughness is a characteristic of a surface that is a function of its geometry. Ultrasonic sensing in air provides range, area, and angle information because the surface geometry determines the characteristics of the echo. The authors introduce the "spatial-angle-filter model" to explain the impact of surface roughness on the echo. On the basis of this model, they design a set of features for use in classifying surfaces. The quality of the features and the classification is measured with the Mahalanobis distance. The resultant system is able to achieve 99.73% classification of a set of 12 surfaces using five features. Disciplines Physical Sciences and MathematicsIndex Terms-Continuous transmission frequency modulated (CTFM) ultrasonic sensor, Mahalanobis classifier, spatial-angle filter, surface roughness, ultrasonic sensing, wheeled mobile robot.

show abstract

Section: Feature Extractionmentioning

confidence: 99%

“…Politis and Probert Smith [6], [7] used CTFM sensors angled to the surface to measure roughness. Using the distribution of energy between the specular and diffuse components of the echo [two-dimensional (2-D) feature vector], they were able to distinguish between six indoor surfaces typical of pathways.…”

Section: Introductionmentioning

confidence: 99%

Classifying Surface Roughness With CTFM Ultrasonic Sensing

McKerrow

Kristiansen²

2006

IEEE Sensors J.

View full text Add to dashboard Cite

show abstract

“…The echo signal can be described as:

S_{R} (t) = A β (λ, R, θ) S_{T} (t - τ)

where β ( λ , R , θ ) is an attenuation factor related to the wavelength λ , the range R , and the incident angle θ . The amplitude of the echo also depends on the surface characteristic of the reflector [6,12]. To focus on the range finding function of the system, it is assumed that the targets are strong reflectors and are within the observable area.…”

Section: Ctfm System Descriptionmentioning

confidence: 99%

“…With its advantages of high precision, broadband, high signal-to-noise ratio, and high quantity of information, the CTFM sonar is capable of detecting multiple targets, classifying primitive indoor targets [11], and even recognizing complex targets such as rough surfaces [12], leafy plants [13], and human faces [14]. However, most of the applications of a CTFM sonar are carried out under stationary conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Yang

2013

Sensors

View full text Add to dashboard Cite

As a ranging sensor, a continuous transmission frequency modulation (CTFM) sonar with its ability for range finding and range profile formation works effectively under stationary conditions. When a relative velocity exists between the target and the sonar, the echo signal is Doppler-shifted. This situation causes the output of the sensor to deviate from the actual target range, thus limiting its applications to stationary conditions only. This work presents an approach for correcting such a deviation. By analyzing the Doppler effect during the propagation process, the sensor output can be corrected by a Doppler factor. To obtain this factor, a conventional CTFM system is slightly modified by adding a single tone signal with a frequency that locates out-of-sweep range of the transmitted signal. The Doppler factor can be extracted from the echo. Both verification experiments and performance tests are carried out. Results indicate the validity of the proposed approach. Moreover, ranging precision under different processing setups is discussed. For adjacent multiple targets, the discrimination ability is influenced by displacement and velocity. A discrimination boundary is provided through an analysis.

show abstract

“…This is in contrast to other systems classifying texture (Bozma and Kuc 1994;Kuc 1997a) that require greater precision. Early work was presented in Politis and Probert (1999). Success rates of more than 99% were achieved using only a single measurement.…”

Section: Introductionmentioning

confidence: 99%

Classification of Textured Surfaces for Robot Navigation Using Continuous Transmission Frequency-Modulated Sonar Signatures

Politis¹,

Probert

2001

The International Journal of Robotics Research

Self Cite

View full text Add to dashboard Cite

Whereas in the past ultrasonic sensors have been largely used only to estimate the proximity of objects and the location and identification of primitive targets in a robot workspace, the development of biomimetic sonar has opened up new possibilities for their application. Broadband sonar echoes have sufficient resolution so that characteristics on reflection, especially geometry and texture, can be distinguished with only a few measurements. In this paper, we describe how a model of texture can be used to distinguish between a number of different surfaces using only a single measurement of each, showing results on a number of surfaces that might be considered typical pathways for a mobile robot, both those with periodicity in pattern and those with statistically homogeneous features. In particular, we consider textures corresponding to hard smooth floors, carpets and asphalts, and surfaces with a repeating pattern made up of tiles. Each random rough surface is modeled using an extension of the Kirchhoff approximation method describing the scattering of the acoustic wave on the surface while the periodic surfaces are modeled assuming distinctive reflections from the tile borders. The continuous transmission frequency-modulated sonar signature corresponding to each class is derived and compared with the experimental measurement. A set of features is extracted that exploits the differences between the surface models, and a hierarchical classification scheme is proposed for recognition.

show abstract

Modeling and classification of rough surfaces using CTFM sonar imaging

Cited by 11 publications

References 4 publications

Classifying Surface Roughness With CTFM Ultrasonic Sensing

Classifying Surface Roughness With CTFM Ultrasonic Sensing

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

Classification of Textured Surfaces for Robot Navigation Using Continuous Transmission Frequency-Modulated Sonar Signatures

Contact Info

Product

Resources

About