`pytransform3d.transformations`.random_transform¶

pytransform3d.transformations.random_transform(rng=Generator(PCG64) at 0x7FA768DBDF20, mean=array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]), cov=array([[1., 0., 0., 0., 0., 0.], [0., 1., 0., 0., 0., 0.], [0., 0., 1., 0., 0., 0.], [0., 0., 0., 1., 0., 0.], [0., 0., 0., 0., 1., 0.], [0., 0., 0., 0., 0., 1.]]))[source]¶

Generate random transform.

Generate $\Delta \boldsymbol{T}_{B_{i+1}{B_i}} \boldsymbol{T}_{{B_i}A}$ , with $\Delta \boldsymbol{T}_{B_{i+1}{B_i}} = Exp(S \theta)$ and $\mathcal{S}\theta \sim \mathcal{N}(\boldsymbol{0}_6, \boldsymbol{\Sigma}_{6 \times 6})$ . The mean $\boldsymbol{T}_{{B_i}A}$ and the covariance $\boldsymbol{\Sigma}_{6 \times 6}$ are parameters of the function.

Note that uncertainty is defined in the global frame B, not in the body frame A.

Parameters:

rngnp.random.Generator, optional (default: random seed 0): Random number generator
meanarray-like, shape (4, 4), optional (default: I): Mean transform as homogeneous transformation matrix.
covarray-like, shape (6, 6), optional (default: I): Covariance of noise in exponential coordinate space.

Returns: