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.
|Original language||American English|
|Title of host publication||Proceedings CVPR 1992 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Publisher||IEEE Computer Society|
|Number of pages||6|
|State||Published - 1992|
|Event||1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1992 - Champaign, United States|
Duration: 15 Jun 1992 → 18 Jun 1992
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1992|
|Period||15/06/92 → 18/06/92|
Bibliographical notePublisher Copyright:
© 1992 IEEE.