Absolute orientation from uncertain point data: a unified approach

Author

Hel-Or, Yaacov ; Werman, Michael

Author_Institution

Inst. of Comput. Sci., Hebrew Univ. of Jerusalem, Israel

fYear

1992

fDate

15-18 Jun 1992

Firstpage

77

Lastpage

82

Abstract

A general and flexible method for fusing and integrating different 2D and 3D measurements for pose estimation is proposed. The 2D measured data are viewed as 3D data with infinite uncertainty in a particular direction. This representation unifies the two categories of the absolute orientation problem into a single problem that varies only in the uncertainty values associated with the measurements. With this paradigm a uniform mathematical formulation of the problem is obtained, and different kinds of measurements that can be fused to obtain a better solution. The method, which is implemented using Kalman filtering, is robust and easily parallelizable

Keywords

Kalman filters; computer vision; filtering and prediction theory; spatial variables measurement; 2D measured data; 3D data; 3D measurements; Kalman filtering; absolute orientation; infinite uncertainty; parallelizable; pose estimation; uncertain point data; uncertainty values; Computer science; Covariance matrix; Filtering; Gratings; Iterative methods; Kalman filters; Noise measurement; Position measurement; Robustness; Sensor fusion;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision and Pattern Recognition, 1992. Proceedings CVPR '92., 1992 IEEE Computer Society Conference on

Conference_Location

Champaign, IL

ISSN

1063-6919

Print_ISBN

0-8186-2855-3

Type

conf

DOI

10.1109/CVPR.1992.223224

Filename

223224