Pose Estimation by Fusing Noisy Data of Different Dimensions

Yacov Hel-Or, Michael Werman

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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 languageAmerican English
Pages (from-to)195-201
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number2
StatePublished - Feb 1995


  • Kalman filter
  • Sensor fusion
  • model based
  • object recognition
  • pose estimation


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