Robot Environment Modeling via Principal Component

A key issue in mobile robot applications involves building a map of the environment to be used by the robot for localization and path planning. In this paper we propose a novel framework for robot map building which is based on principal component regression, a statistical method for extracting low-dimensional dependencies between a set of input and target values. A supervised set of robot positions (inputs) and associated high-dimensional sensor measurements (targets) are assumed. A set of globally uncorrelated features of the original sensor measurements are obtained by applying principal component analysis on the target set. A parametrized model of the conditional density function of the sensor features given the robot positions is built based on an unbiased estimation procedure that ts interpolants for both the mean and the variance of each feature independently. The simulation results show that the average Bayesian localization error is an increasing function of the principal component index.