"Random" generation of rotation matrices

For a current project, I need to generate several $3\times 3$ rotation matrices for input into an algorithm. I thought I might go about this by randomly generating the number of elements needed to define a rotation matrix and then calculating the remaining elements from them. Does anyone know of an algorithm for calculating the remaining elements once a defining set of elements is given? Or does anyone know of a better way to go about this? Thanks.

Solution 1:

I had this question and I solved it via QR decomposition of 3x3 matrices with normally distributed random numbers. If you have matlab, use: [Q,R] = qr(randn(3));

Q is a uniformly random rotation matrix. For more info, refer to: M. Ozols, “How to generate a random unitary matrix,” 2009.

Solution 2:

Rotation matrices can be uniquely defined by a vector and a rotation angle. To generate the vector, you can use grandom spherical coordinates $\phi$ and $\theta$. Thus you should first generate random angles by using:

$$\theta = \arccos(2u_1 - 1)$$ $$\phi = 2\pi u_2$$ Where $u_1,u_2$ are uniformly distributed in $[0,1]$. This will give you a vector around which to rotate. Then, randomly decide the amount of rotation $\psi\in[0,2\pi]$.

Solution 3:

I have had the same exact problem myself a while ago so I point you to this which says it very succinctly with plenty of references.

Solution 4:

This is an interesting question! If the "random matrix" is being used for any sort of Monte-Carlo testing, it is important that a sequence of "random matrices" produced by whatever (as von Neumann noted, putting us in a state of sin if we claim too much about it) pseudo-random device, be at least demonstrably equi-distributed in the rotation group $SO(3)$.

I have no serious operational quibble with other answers, which are certainly thoughtful and productive, but/and I might object that they are ad-hoc, so offering no a-priori promise of any genuine pseudo-randomness.

If it does matter to have a more-certifiable pseudo-randomness, the following device lends itself more to proof, for random 3-D rotations. Use the fact that Hamiltonian $\mathbb H$ quaternions give 3D rotations in at least one way, namely, identify $\mathbb R^3$ with purely imaginary quaternions, and let $g\in \mathbb H^\times$ act on purely-imaginary quaternions $x$ by $g\cdot x=gxg^{-1}$.

In that set-up, it's not too hard to prove various "pseudo-randomness" (equi-distribution) properties.

(Edit: For example, choose elements of $\mathbb H$ pseudo-randomly in some ball of radius $R$ according to some volume-equidistribution "rule", and let $R\rightarrow +\infty$...)

Solution 5:

A rotation matrix R is the same as an orthonormal basis that preserves orientation ($\det(R)=1$). Hence, to create a uniform distributed random rotation matrix, we need to pick three orthogonal random unit vectors, make sure that the orientation is correct and concatenate them into a matrix.

First pick two random unit vectors $u$ and $v$. For numerical stability, ensure that $|u\cdot v| < 0.99$. Now project $v$ onto the plane perpendicular to $u$, i.e. subtract $(v\cdot u)u$ from $v$ and normalize the result. The last axis is fixed by $u$ and $v$, hence we can compute $w$ using the cross product: $w = u \times v$. The resulting rotation matrix is $R=(u\ v\ w)$.