A statistical estimation method for segmentation of sonar range data

Abstract

In this paper, we describe how to deal with an important sensorial activity that ultrasonic echo-locating systems for mobile robot navigation have often to perform, namely the extraction of straight line segments from range data and the accurate localization of the corresponding planar targets. It is commonplace that range data segmentation starts with using least squares interpolation algorithms for obtaining straight line segments: it is our goal to prove that caution must be called for in order to avoid somewhat misleading results. The case study concerns the use of a linear array formed by three ultrasonic transducers in a 2D specular environment composed of line and point acoustic targets.

The segmentation algorithm we propose is subdivided into two functionally distinct modules, namely identification and localization. The identification module is based on a sequential hypothesis testing between alternative hypotheses that explain the sonar range data as originated from line or point targets. With regard to the localization module, we demonstrate that widely used approaches to sensor modeling are, to some extent, deceptively simple: the estimation accuracy for the localization of planar objects may be decreased by the inability of some traditional sonar sensor models to take properly into account the specularity of reflections. A physically based model of acoustic range sensors acting in specular environments allows us to design a localization module which is capable of producing accurate and unbiased estimates of the parameters of a planar geometric feature.

The proposed theoretical framework is validated by the results of some experiments carried out with a spatial locating system consisting of a rotating linear array of three ultrasonic transducers.