Rotations.jl

3D rotations made easy in Julia

This package implements various 3D rotation parameterizations and defines conversions between them. At their heart, each rotation parameterization is a 3×3 unitary (orthogonal) matrix (based on the StaticArrays.jl package), and acts to rotate a 3-vector about the origin through matrix-vector multiplication.

While the RotMatrix type is a dense representation of a 3×3 matrix, we also have sparse (or computed, rather) representations such as quaternions, angle-axis parameterizations, and Euler angles.

For composing rotations about the origin with other transformations, see also the CoordinateTransformations.jl or AffineTransforms.jl packages.

Example Usage

using Rotations, StaticArrays

# create the null rotation (identity matrix)
id = eye(RotMatrix{3, Float64})

# create a random rotation matrix (uniformly distributed over all 3D rotations)
r = rand(RotMatrix{3}) # uses Float64 by default

# create a point
p = SVector(1.0, 2.0, 3.0) # from StaticArrays.jl, but could use any AbstractVector...

# convert to a quaternion (Quat) and rotate the point
q = Quat(r)
p_rotated = q * p

# Compose rotations
q2 = rand(Quat)
q3 = q * q2

# Take the inverse (equivalent to transpose)
q_inv = q'
q_inv == inv(q)
p ≈ q_inv * (q * p)
q4 = q3 / q2  # q4 = q3 * inv(q2)
q5 = q3 \ q2  # q5 = inv(q3) * q2

# convert to a Stereographic quaternion projection (recommended for applications with differentiation)
spq = SPQuat(r)

# convert to the Angle-axis parameterization, or related Rodrigues vector
aa = AngleAxis(r)
rv = RodriguesVec(r)
ϕ = rotation_angle(r)
v = rotation_axis(r)

# convert to Euler angles, composed of X/Y/Z axis rotations (Z applied first)
# (all combinations of "RotABC" are defined)
r_xyz = RotXYZ(r)

# Rotation about the X axis by 0.1 radians
r_x = RotX(0.1)

# Composing axis rotations together automatically results in Euler parameterization
RotX(0.1) * RotY(0.2) * RotZ(0.3) === RotXYZ(0.1, 0.2, 0.3)

# Can calculate Jacobian - derivatives of rotations with respect to parameters
j1 = Rotations.jacobian(RotMatrix, q) # How does the matrix change w.r.t the 4 Quat parameters?
j2 = Rotations.jacobian(q, p) # How does the rotated point q*p change w.r.t. the 4 Quat parameters?
# ... all Jacobian's involving RotMatrix, SPQuat and Quat are implemented
# (SPQuat is ideal for optimization purposes - no constaints/singularities)

Rotation Parameterizations

1. Rotation Matrix RotMatrix{N, T}

   An N x N rotation matrix storing the rotation. This is a simple wrapper for a StaticArrays SMatrix{N,N,T}. A rotation matrix R should have the property I = R * R', but this isn't enforced by the constructor. On the other hand, all the types below are guaranteed to be "proper" rotations for all input parameters (equivalently: parity conserving, in SO(3), det(r) = 1, or a rotation without reflection).

2. Arbitrary Axis Rotation AngleAxis{T}

   A 3D rotation with fields theta, axis_x, axis_y, and axis_z to store the rotation angle and axis of the rotation. Like all other types in this package, once it is constructed it acts and behaves as a 3×3 AbstractMatrix. The axis will be automatically renormalized by the constructor to be a unit vector, so that theta always represents the rotation angle in radians.

3. Quaternions Quat{T}

   A 3D rotation parameterized by a unit quaternion. Note that the constructor will renormalize the quaternion to be a unit quaternion, and that although they follow the same multiplicative algebra as quaternions, it is better to think of Quat as a 3×3 matrix rather than as a quaternion number.

4. Rodrigues Vector RodriguesVec{T}

   A 3D rotation encoded by an angle-axis representation as angle * axis.
   This type is used in packages such as OpenCV.

   Note: If you're differentiating a Rodrigues Vector check the result is what you expect at theta = 0. The first derivative of the rotation should behave, but higher-order derivatives of it (as well as parameterization conversions) should be tested. The Stereographic Quaternion Projection is the recommended three parameter format for differentiation.

5. Stereographic Projection of a unit Quaternion SPQuat{T}

   A 3D rotation encoded by the stereographic projection of a unit quaternion. This projection can be visualized as a pin hole camera, with the pin hole matching the quaternion [-1,0,0,0] and the image plane containing the origin and having normal direction [1,0,0,0]. The "null rotation" Quaternion(1.0,0,0,0) then maps to the SPQuat(0,0,0)

   These are similar to the Rodrigues vector in that the axis direction is stored in an unnormalized form, and the rotation angle is encoded in the length of the axis. This type has the nice property that the derivatives of the rotation matrix w.r.t. the SPQuat parameters are rational functions, making the SPQuat type a good choice for differentiation / optimization.

6. Cardinal axis rotations RotX{T}, RotY{T}, RotZ{T}

   Sparse representations of 3D rotations about the X, Y, or Z axis, respectively.

7. Two-axis rotations RotXY{T}, etc

   Conceptually, these are compositions of two of the cardinal axis rotations above, so that RotXY(x, y) == RotX(x) * RotY(y) (note that the order of application to a vector is right-to-left, as-in matrix-matrix-vector multiplication: RotXY(x, y) * v == RotX(x) * (RotY(y) * v)).

8. Euler Angles - Three-axis rotations RotXYZ{T}, RotXYX{T}, etc

   A composition of 3 cardinal axis rotations is typically known as a Euler angle parameterization of a 3D rotation. The rotations with 3 unique axes, such as RotXYZ, are said to follow the Tait Byran angle ordering, while those which repeat (e.g. EulerXYX) are said to use Proper Euler angle ordering.

   Like the two-angle versions, read the application of the rotations along the static cardinal axes to a vector from right-to-left, so that RotXYZ(x, y, z) * v == RotX(x) * (RotY(x) * (RotZ(z) * v)). This is the "extrinsic" representation of an Euler-angle rotation, though if you prefer the "intrinsic" form it is easy to use the corresponding RotZYX(z, y, x).

Import / Export

All parameterizations can be converted to and from (mutable or immutable) 3×3 matrices, e.g.

using StaticArrays, Rotations

# export
q = Quat(1.0,0,0,0)
matrix_mutable = Array(q)
matrix_immutable = SMatrix{3,3}(q)

# import
q2 = Quaternion(matrix_mutable)
q3 = Quaternion(matrix_immutable)

Notes

This package assumes active (right handed) rotations where applicable.

Why use immutables / StaticArrays?

They're faster (Julia's Array and BLAS aren't great for 3x3 matrices) and don't need preallocating or garbage collection. For example, see this benchmark case where we get a 20× speedup:

julia> cd(Pkg.dir("Rotations") * "/test")

julia> include("benchmark.jl")

julia > BenchMarkRotations.benchmark_mutable()
Rotating using mutables (Base.Matrix and Base.Vector):
  0.124035 seconds (2 allocations: 224 bytes)
Rotating using immutables (Rotations.RotMatrix and StaticArrays.SVector):
  0.006006 seconds