How to interpolate rotations?

I have two vectors describing rotations; a start rotation A and a target rotation B. How would I best go about interpolating A by a factor F to approach B?

Using a simple lerp on the vectors fails to work when more than one dimension needs to be interpolated (i.e. produces undesirable rotations). Maybe building quaternions from the rotation 开发者_运维百科vectors and using slerp is the way to go. But how, then, could I extract a vector describing the new rotation from the resulting quaternion?

Thanks in advance.

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Since I don't seem to understand your question, here is a little SLERP implementation in python using numpy. I plotted the results using matplotlib (v.99 for Axes3D). I don't know if you can use python, but does look like your SLERP implementation? It seems to me to give fine results ...

from numpy import *
from numpy.linalg import norm

def slerp(p0, p1, t):
        omega = arccos(dot(p0/norm(p0), p1/norm(p1)))
        so = sin(omega)
        return sin((1.0-t)*omega) / so * p0 + sin(t*omega)/so * p1


# test code
if __name__ == '__main__':
    pA = array([-2.0, 0.0, 2.0])
    pB = array([0.0, 2.0, -2.0])

    ps = array([slerp(pA, pB, t) for t in arange(0.0, 1.0, 0.01)])

    from pylab import *
    from mpl_toolkits.mplot3d import Axes3D
    f = figure()
    ax = Axes3D(f)
    ax.plot3D(ps[:,0], ps[:,1], ps[:,2], '.')
    show()

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A simple LERP (and renormalizing) only works fine when the vectors are very close together, but will result in unwanted results when the vectors are further apart.

There are two options:

Simple cross-products:

Determine the axis n that is orthogonal to both A and B using a cross product (take care when the vectors are aligned) and calculate the angle a between A and B using a dot product. Now you can simply approach B by letting a go from 0 to a (this will be aNew and applying the rotation of aNew about axis n on A.

Quaternions:

Calculate the quaternion q that moves A to B, and interpolate q with the identity quaternion I using SLERP. The resulting quaternion qNew can then be applied on A.

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Well, your slerp approach would work and is probably computationally most efficient (even though it's a bit tough to understand). To get back from the quaternions to the vector, you'll need to use a set of formulas you can find here.

There's also a bit of relevant code here, although I don't know if it corresponds to the way you have your data represented.

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If you have decided to go with Quaternions (which will slerp very nicely), see my answer here on resources for implementing Quaternions: Rotating in OpenGL relative to the viewport

You should find plenty of examples in the links in that post.