Using stereoscopic cameras, I track a certain object through it's path in space. For every frame, I compute it's pose in 3D. I can represent it's pose by either a translation vector + rotation matrix or a 6 element parameter vector (X, Y, Z, roll, pitch, yaw).
The problem is that the output generated by the cameras is noisy, and I'd like to filter some of that noise off by smoothing the poses in 3D.
Any pointers on that? What fitting works better for multidimensional problems?
Some more information: in my case, the XYZ path can probably be fitted with a 2nd order curve, and roll, pitch and yaw also demonstrate smooth change over samples.