Title :
Pose estimation by fusing noisy data of different dimensions
Author :
Hel-Or, Yacov ; Werman, Michael
Author_Institution :
Inst. of Comput. Sci., Hebrew Univ., Jerusalem, Israel
fDate :
2/1/1995 12:00:00 AM
Abstract :
A method for fusing and integrating different 2D and 3D measurements for pose estimation is proposed. The 2D measured data is viewed as 3D data with infinite uncertainty in particular directions. The method is implemented using Kalman filtering. It is robust and easily parallelizable
Keywords :
Kalman filters; filtering theory; object recognition; sensor fusion; 2D measurements; 3D measurement; Kalman filtering; infinite uncertainty; noisy data; pose estimation; Covariance matrix; Filtering; Geometry; Iterative methods; Noise measurement; Object recognition; Particle measurements; Position measurement; Robustness; Solids;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
