Cataloged from PDF version of article.This study compares the performances of various techniques for the differentiation
and localization of commonly encountered features in indoor environments,
such as planes, corners, edges, and cylinders, possibly with different surface properties,
using simple infrared sensors. The intensity measurements obtained from
such sensors are highly dependent on the location, geometry, and surface properties
of the reflecting feature in a way that cannot be represented by a simple
analytical relationship, therefore complicating the localization and differentiation
process. The techniques considered include rule-based, template-based, and neural
network-based target differentiation, parametric surface differentiation, and
statistical pattern recognition techniques such as parametric density estimation,
various linear and quadratic classifiers, mixture of normals, kernel estimator,
k-nearest neighbor, artificial neural network, and support vector machine classi-
fiers. The geometrical properties of the targets are more distinctive than their
surface properties, and surface recognition is the limiting factor in differentiation.
Mixture of normals classifier with three components correctly differentiates three
types of geometries with different surface properties, resulting in the best performance
(100%) in geometry differentiation. For a set of six surfaces, we get a correct
differentiation rate of 100% in parametric differentiation based on reflection
modeling. The results demonstrate that simple infrared sensors, when coupled
with appropriate processing, can be used to extract substantially more information
than such devices are commonly employed for. The demonstrated system
would find application in intelligent autonomous systems such as mobile robots
whose task involves surveying an unknown environment made of different geometry
and surface types. Industrial applications where different materials/surfaces
must be identified and separated may also benefit from this approach.Aytaç, TayfunPh.D
