Pose Estimation by Fusing Noisy Data of Different Dimensions

Yacov Hel-Or; Michael Werman

doi:10.1109/34.368169

Pose Estimation by Fusing Noisy Data of Different Dimensions

Yacov Hel-Or, Michael Werman

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

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.

Original language	English
Pages (from-to)	195-201
Number of pages	7
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	17
Issue number	2
DOIs	https://doi.org/10.1109/34.368169
State	Published - Feb 1995

Keywords

Kalman filter
Sensor fusion
model based
object recognition
pose estimation

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10.1109/34.368169

Cite this

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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.",

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Pose Estimation by Fusing Noisy Data of Different Dimensions

Abstract

Keywords

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Corrections to 'Pose Estimation by Fusing Noisy Data of Different Dimensions'.

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Pose Estimation by Fusing Noisy Data of Different Dimensions

Abstract

Keywords

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Corrections to 'Pose Estimation by Fusing Noisy Data of Different Dimensions'.

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