2
$\begingroup$

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.

  • 0
    Smoothing and curve fitting are two different problems. For the former, a common approach is to do a weighted moving average of your data, for example by convolving with a Gaussian kernel. In either case, smoothing/fitting orientation data is going to be tricky; you probably don't want to work directly with Euler angles, because near the "gimbal lock" singularity, the angles will vary wildly with even small amounts of noise.2010-10-13
  • 1
    Also, I don't see why this question has a vote to close as "off topic". Geometry, signal smoothing, and curve fitting are all part of mathematics.2010-10-13

1 Answers 1