Automotive system for remote surface classification

In this paper we shall discuss a novel approach to road surface recognition, based on the analysis of backscattered microwave and ultrasonic signals. The novelty of our method is sonar and polarimetric radar data fusion, extraction of features for separate swathes of illuminated surface (segmentation), and using of multi-stage artificial neural network for surface classification. The developed system consists of 24 GHz radar and 40 kHz ultrasonic sensor. The features are extracted from backscattered signals and then the procedures of principal component analysis and supervised classification are applied to feature data. The special attention is paid to multi-stage artificial neural network which allows an overall increase in classification accuracy. The proposed technique was tested for recognition of a large number of real surfaces in different weather conditions with the average accuracy of correct classification of 95%. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real conditions

Feature extraction from a broadband sonar sensor for mapping structured environments efficiently

In this paper, we describe the use of a broadband, frequency-modulated 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