Position Estimation for a Mobile Robot From Principal Components of Laser Range Data

This paper describes a new approach to indoor mobile robot position estimation, based on principal component analysis of laser range data. The eigenspace deened by the principal components of a number of range data sets describes the symetries in the data. Building structures ooer a small number of main axes of symetry as caused by objects such as walls. As a consequence, the dimension of the eigenspace can be reduced to few axes which describe these symetries. By transforming a new data set in the low{dimensional eigenspace, every potential position at which the data where taken as well as the probability of this position can be derived from the sourrounding training data sets, of which the positions are known. The paper describes the principal component analysis of sets of range data and discusses its characteristics in indoor environments. It compares diierent methods to generate position hypothesis and discusses the question of noisy measurements and scene changes. Finally a probablistic model is proposed to integrate sequences of observations in order to reconstruct robot trajectories. The advantage of the approach is the transformation of high{dimensional data sets in a low dimensional eigenspace. The reduction in complexity achieved by this transformation allows to localize the robot independant of other sources of position estimation (such as odometry) using adjacent measurements to resolve ambiguities. It is also possible to survey and correct an underlaying position estimation technique such as odometry.