jacobian[index]=axis.cross(tempvec);
// J * JT jacobian = j
const real x=jacobian[index].x;
const real y=jacobian[index].y;
const real z=jacobian[index].z;
const real xy=x*y;
const real xz=x*z;
const real yz=y*z;
tempmat[0][0]+=x*x;
tempmat[0][1]+=xy;
tempmat[0][2]+=xz;
tempmat[1][0]+=xy;
tempmat[1][1]+=y*y;
tempmat[1][2]+=yz;
tempmat[2][0]+=xz;
tempmat[2][1]+=yz;
tempmat[2][2]+=z*z;
What is it trying to do?
Author
Hi Guys,
With this code snippet,
You'll do this sort of thing alot when you are calculating the inverse kinematics of a system. Check out: http://math.ucsd.edu/~sbuss/ResearchWeb/ikmethods/iksurvey.pdf
Author
Quote:Original post by mmakrzem
You'll do this sort of thing alot when you are calculating the inverse kinematics of a system. Check out: http://math.ucsd.edu/~sbuss/ResearchWeb/ikmethods/iksurvey.pdf
Sorry, my english is bad, so there was a misunderstanding here.
I'd like to know if the code snippet is doing a multiplication of a matrix with its transpose..
Thanks :)
Jack
That matrix formed by x y and z is the product of (x y z) as a column with (x y z) as a row. In that sense, it is the product of a matrix with its transpose.
Does that answer your question?
Does that answer your question?
You can see it as a tensor product( actually it is ), that is to say, the multiplication of a vector by its transpose:
A = v * transpose( v ) ( A is a 3x3 matrix and v a column vector, that is to say, a 3x1 matrix )
or
A = transpose( v ) * v ( A is a 3x3 matrix and v a row vector, that is to say, a 1x3 matrix )
A = v * transpose( v ) ( A is a 3x3 matrix and v a column vector, that is to say, a 3x1 matrix )
or
A = transpose( v ) * v ( A is a 3x3 matrix and v a row vector, that is to say, a 1x3 matrix )
This topic is closed to new replies.
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