Position estimation using principal components of range data
نویسندگان
چکیده
منابع مشابه
Position Estimation Using Principal Components of Range Data
1 sensors is to construct a structural description from sensor data and to match this description to a previously acquired model [Crowley 85]. An alternative is to project individual range measurements onto a previously acquired model [Leonard and Durrant-Whyte 91]. It is also possible to fuse range measurements directly using occupancy grids [Elfes 86], [Schiele 94]. Recently it has been shown...
متن کاملPosition 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 e...
متن کاملBayesian Estimation of Principal Components for Functional Data
Abstract. The area of principal components analysis (PCA) has seen relatively few contributions from the Bayesian school of inference. In this paper, we propose a Bayesian method for PCA in the case of functional data observed with error. We suggest modeling the covariance function by use of an approximate spectral decomposition, leading to easily interpretable parameters. We study in depth the...
متن کاملPersian Handwriting Analysis Using Functional Principal Components
Principal components analysis is a well-known statistical method in dealing with large dependent data sets. It is also used in functional data for both purposes of data reduction as well as variation representation. On the other hand "handwriting" is one of the objects, studied in various statistical fields like pattern recognition and shape analysis. Considering time as the argument,...
متن کاملNonlinear Regression Estimation Using Subset-based Kernel Principal Components
We study the estimation of conditional mean regression functions through the so-called subset-based kernel principal component analysis (KPCA). Instead of using one global kernel feature space, we project a target function into different localized kernel feature spaces at different parts of the sample space. Each localized kernel feature space reflects the relationship on a subset between the r...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Robotics and Autonomous Systems
سال: 1998
ISSN: 0921-8890
DOI: 10.1016/s0921-8890(98)00013-x