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<p><it>Abstract</it>— 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.</p>
Sensor fusion, Kalman filter, pose estimation, model based, object recognition.
Yacov Hel-Or, Michael Werman, "Pose Estimation by Fusing Noisy Data of Different Dimensions", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 17, no. , pp. 195-201, February 1995, doi:10.1109/34.368169
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