Getting OrientedIn this tutorial, we will investigate specific problems with regard to orienting
objects.Gimbal LockRemember a few tutorials back, when we said that a rotation matrix isn't a rotation
matrix at all, that it is an orientation matrix? We also said that forgetting this can
come back to bite you. Well, here's likely the most common way.Normally, when dealing with orienting an object like a plane or spaceship in 3D space,
you want to orient it based on 3 rotations about the 3 main axes. The obvious way to do
this is with a series of 3 rotations. This means that the program stores 3 angles, and
you generate a rotation matrix by creating 3 rotation matrices based on these angles and
concatenating them. Indeed, the 3 angles often have special names, based on common
flight terminology: yaw, pitch, and roll.Pitch is the rotation that raises or lowers the front of the object. Yaw is the
rotation that turns the object left and right. Roll is the rotation that spins it around
the direction of motion. These terms neatly duck the question of what the axes
technically are; they are defined in terms of semantic axes (front, left, direction of
motion, etc), rather than a specific model space. So in one model space, roll might be a
rotation about the X axis, but in another, it might be a rotation about the Z
axis.One of the first problems you will note is that the order you apply these rotations
matter. As previously stated, a rotation matrix is an orientation
transform. Each transform defines a new coordinate system, and the next transform is
based on an object in the new space. For example, if we apply the
roll first, we have now changed what the axis for the subsequent yaw is.You can use any order that you like, so long as you understand what these angles mean.
If you apply the roll first, your yaw and pitch must be in terms of the new roll
coordinate system, and not the original coordinate system. That is, a change that is
intended to only affect the roll of the final space may need yaw or pitch changes to
allow it to have the same apparent orientation (except for the new roll, of
course).But there is a far more insidious problem lurking in here. And this problem happens
anytime you compute a final rotation from a series of 3 rotations about axes
perpendicular to each other.The tutorial project Gimbal Lock illustrates this
problem. Because the problem was first diagnosed with a physical device called a gimbal, the problem has
become known as gimbal lock.A gimbal is a pivoted support that provides the ability to rotate in one axis. A
gimbal can be mounted within another gimbal. The Gimbal Lock project has a set of 3
square gimbals, each with a pivot axis that is perpendicular to the other two. This
effectively mimics the common yaw/pitch/roll angle setup.You can control the orientation of each gimbal separately. The w and
s keys control the outer gimbal, the a and
d keys control the middle gimbal, and the q and
e keys control the inner gimbal. If you just want to see (and
affect) the orientation of the ship, press the SpaceBar to toggle
drawing the gimbal rings.The first thing you discover when attempting to use the gimbals to orient the ship is
that the yaw, pitch, and roll controls of the gimbal change each time you move one of
them. That is, when the gimbal arrangement is in the original position, the outer gimbal
controls the pitch. But if you move the middle gimbal, it no longer controls
only the pitch. Orienting the ship is very unintuitive.The bigger is what happens when two of the gimbals are parallel with one
another:Recall that the purpose of the three gimbals is to be able to adjust one of the three
angles and orient the object in a particular direction. In a flight-simulation game, the
player would have controls that would change their yaw, pitch, and roll. However, look
at this picture.Given the controls of these gimbals, can you cause the object to pitch up and down?
That is, move its nose up and down from where it is currently? Only slightly; you can
use the middle gimbal, which has a bit of pitch rotation. But that isn't much.The reason we don't have as much freedom to orient the object is because the outer and
inner gimbals are now rotating about the same axis. Which means you
really only have two gimbals to manipulate in order to orient the red gimbal. And 3D
orientation cannot be fully controlled with only 2 axial rotations, with only 2
gimbals.When gimbals are in such a position, you have what is known as gimbal
lock; you have locked one of the gimbals to another, and now both cause
the same effect.RenderingBefore we find a solution to the problem, let's review the code. Most of it is
nothing you haven't seen elsewhere, so this will be quick.There is no explicit camera matrix set up for this example; it is too simple for
that. The three gimbals are loaded from mesh files as we saw in our last tutorial.
They are built to fit into the above array. The ship is also from a mesh
file.The rendering function looks like this:Gimbal Lock Display Codevoid display()
{
glClearColor(0.0f, 0.0f, 0.0f, 0.0f);
glClearDepth(1.0f);
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
Framework::MatrixStack currMatrix;
currMatrix.Translate(glm::vec3(0.0f, 0.0f, -200.0f));
currMatrix.RotateX(g_angles.fAngleX);
DrawGimbal(currMatrix, GIMBAL_X_AXIS, glm::vec4(0.4f, 0.4f, 1.0f, 1.0f));
currMatrix.RotateY(g_angles.fAngleY);
DrawGimbal(currMatrix, GIMBAL_Y_AXIS, glm::vec4(0.0f, 1.0f, 0.0f, 1.0f));
currMatrix.RotateZ(g_angles.fAngleZ);
DrawGimbal(currMatrix, GIMBAL_Z_AXIS, glm::vec4(1.0f, 0.3f, 0.3f, 1.0f));
glUseProgram(theProgram);
currMatrix.Scale(3.0, 3.0, 3.0);
currMatrix.RotateX(-90);
//Set the base color for this object.
glUniform4f(baseColorUnif, 1.0, 1.0, 1.0, 1.0);
glUniformMatrix4fv(modelToCameraMatrixUnif, 1, GL_FALSE, glm::value_ptr(currMatrix.Top()));
g_pObject->Render("tint");
glUseProgram(0);
glutSwapBuffers();
}The translation done first acts as our camera matrix: positioning the objects far
enough away to be comfortably visible. From there, the three gimbals are drawn, with
their appropriate rotations. Since each rotation applies to the previous one, the
final orientation is given to the last gimbal.The DrawGimbal function does some rotations of its own, but
this is just to position the gimbals properly in the array. The gimbals are given a
color programmatically, which is the 3rd parameter to
DrawGimbal.After building up the rotation matrix, we draw the ship. We use a scale to make it
reasonably large, and then rotate it so that it points in the correct direction
relative to the final gimbal. In model space, the ship faces the +Z axis, but the
gimbal faces the +Y axis. So we needed a change of coordinate system.QuaternionsSo gimbals, 3 accumulated axial rotations, don't really work very well for orienting
an object. How do we fix this problem?Part of the problem is that we are trying to store an orientation as a series of 3
accumulated axial rotations. Orientations are orientations, not
rotations. And certainly not a series of them. So we need to treat the orientation of
the ship as an orientation.The first thought towards this end would be to keep the orientation as a matrix. When
the time comes to modify the orientation, we simply apply a transformation to this
matrix, storing the result as the new current orientation.This means that every yaw, pitch, and roll applied to the current orientation will be
relative to that current orientation. Which is precisely what we need. If the user
applies a positive yaw, you want that yaw to rotate them relative to where they are
current pointing, not relative to some fixed coordinate system.There are a few downsides to this approach. First, a 4x4 matrix is rather larger than
3 floating-point angles. But a much more difficult issue is that successive
floating-point math can lead to errors. If you keep accumulating successive
transformations of an object, once every 1/30th of a second for a period of several
minutes or hours, these floating-point errors start accumulating. Eventually, the
orientation stops being a pure rotation and starts incorporating scale and skewing
characteristics.The solution here is to re-orthonormalize the matrix after applying each transform. A
coordinate system (which a matrix defines) is said to be
orthonormal if the basis vectors are of unit length (no
scale) and each axis is perpendicular to all of the others.Unfortunately, re-orthonormalizing a matrix is not a simple operation. You could try
to normalize each of the axis vectors with typical vector normalization, but that
wouldn't ensure that the matrix was orthonormal. It would remove scaling, but the axes
wouldn't be guaranteed to be perpendicular.Orthonormalization is certainly possible. But there are better solutions. Such as using
something called a quaternion.A quaternion is (for the purposes of this conversation) a 4-dimensional vector that is
treated in a special way. Any pure orientation change from one coordinate system to
another can be represented by a rotation about some axis by some angle. A quaternion is
a way of encoding this angle/axis rotation:Angle/Axis to QuaternionAssuming the axis itself is a unit vector, this will produce a unit
quaternion. That is, a quaternion with a length of 1.Quaternions can be considered to be two parts: a vector part and a scalar part. The
vector part are the first three components, when displayed in the order above. The
scalar part is the last part.Quaternion MathQuaternions are equivalent to orientation matrices. You can compose two
orientation quaternions using a special operation called quaternion
multiplication. Given the quaternions a and
b, the product of them is:Quaternion MultiplicationIf the two quaternions being multiplied represent orientations, then the product
of them is a composite orientation. This works like matrix multiplication, except
only for orientations. Like matrix multiplication, quaternion multiplication is
associative ((a*b) * c = a * (b*c)), but not commutative (a*b != b*a).The main difference between matrices and quaternions that matters for our needs is
that it is easy to keep a quaternion normalized. Simply perform a vector
normalization on it after every few multiplications. This enables us to add numerous
small rotations together without numerical precision problems showing up.There is one more thing we need to be able to do: convert a quaternion into a
rotation matrix. While we could convert a unit quaternion back into angle/axis
rotations, it's much preferable to do it directly:Quaternion to MatrixThis does look suspiciously similar to the formula for generating a matrix from an
angle/axis rotation.Composition TypeSo our goal is to compose successive rotations into a final orientation. When we
want to increase the pitch, for example, we will take the current orientation and
multiply into it a quaternion that represents a pitch rotation of a few degrees. The
result becomes the new orientation.But which side do we do the multiplication on? Quaternion multiplication is not
commutative, so this will have an affect on the output. Well, it works exactly like
matrix math.Our positions (p) are in model space. We are transforming them into world space.
The current transformation matrix is represented by the orientation O. Thus, to
transform points, we use O*pNow, we want to adjust the orientation O by applying some small pitch change.
Well, the pitch of the model is defined by model space. Therefore, the pitch change
(R) is a transformation that takes coordinates in model space and transforms them to
the pitch space. So our total transformation is O*R*p; the new orientation is O*R.Yaw Pitch RollWe implement this in the Quaternion YPR
tutorial. This tutorial doesn't show gimbals, but the same controls exist for yaw,
pitch, and roll transformations. Here, pressing the SpaceBar will
switch between right-multiplying the YPR values to the current orientation and
left-multiplying them. Post-multiplication will apply the YPR transforms from
world-space.The rendering code is pretty straightforward.Quaternion YPR Displayvoid display()
{
glClearColor(0.0f, 0.0f, 0.0f, 0.0f);
glClearDepth(1.0f);
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
Framework::MatrixStack currMatrix;
currMatrix.Translate(glm::vec3(0.0f, 0.0f, -200.0f));
currMatrix.ApplyMatrix(glm::mat4_cast(g_orientation));
glUseProgram(theProgram);
currMatrix.Scale(3.0, 3.0, 3.0);
currMatrix.RotateX(-90);
//Set the base color for this object.
glUniform4f(baseColorUnif, 1.0, 1.0, 1.0, 1.0);
glUniformMatrix4fv(modelToCameraMatrixUnif, 1, GL_FALSE, glm::value_ptr(currMatrix.Top()));
g_pShip->Render("tint");
glUseProgram(0);
glutSwapBuffers();
}Though GLSL does not have quaternion types or quaternion arithmetic, the GLM math
library provides both. The g_orientation variable is of the type
glm::fquat, which is a floating-point quaternion. The
glm::mat4_cast function converts a quaternion into a 4x4
rotation matrix. This stands in place of the series of 3 rotations used in the last
tutorial.In response to keypresses, g_orientation is modified, applying
a transform to it. This is done with the OffsetOrientation
function.OffsetOrientation Functionvoid OffsetOrientation(const glm::vec3 &_axis, float fAngDeg)
{
float fAngRad = Framework::DegToRad(fAngDeg);
glm::vec3 axis = glm::normalize(_axis);
axis = axis * sinf(fAngRad / 2.0f);
float scalar = cosf(fAngRad / 2.0f);
glm::fquat offset(scalar, axis.x, axis.y, axis.z);
if(g_bRightMultiply)
g_orientation = g_orientation * offset;
else
g_orientation = offset * g_orientation;
g_orientation = glm::normalize(g_orientation);
}This generates the offset quaternion from an angle and axis. Since the axis is
normalized, there is no need to normalize the resulting offset
quaternion. Then the offset is multiplied into the orientation, and the result is
normalized.In particular, pay attention to the difference between right multiplication and
left multiplication. When you right-multiply, the offset orientation is in model
space. When you left-multiply, the offset is in world space.
Both of these can be useful for different purposes.Camera-Relative OrientationAs useful as model and world space offsetting is, there is one more space that it
might be useful to orient from. Camera-space.This is primarily useful in modelling applications, but it can have other applications
as well. In such programs, as the user spins the camera around to different angles, the
user may want to transform the object relative to the direction of the view.In order to understand the solution to doing this, let's frame the problem explicitly.
We have positions (p) in model space. These positions will be transformed by a current
model-to-world orientation (O), and then by a final camera matrix (C). Thus, our
transform equation is C*O*p.We want to apply an orientation offset (R), which takes points in camera-space. If we
wanted to apply this to the camera matrix, it would simply be multiplied by the camera
matrix: R*C*O*p. That's nice and all, but we want to apply a transform to O, not to
C.Therefore, we need to use our transforms to generate a new orientation offset (N),
which will produce the same effect:C*N*O = R*C*OInversionIn order to solve this, we need to introduce a new concept. Given a matrix M,
there may be a matrix N such that MN = I, where I is the identity matrix. If there is such a matrix, the
matrix N is called the inverse matrix of M. The notation for
the inverse matrix of M is M-1. The symbol
-1 does not mean to raise the
matrix to the -1 power; it means to invert it.The matrix inverse can be analogized to the scalar multiplicative inverse (ie:
reciprocal). The scalar multiplicative inverse of X is the number Y such that XY = 1.In the case of the scalar inverse, this is very easy to solve for: Y = 1/X. Easy though this may be, there are values of X for which there is
no multiplicative inverse. OK, there's one such real value of
X: 0.The case of the inverse matrix is much more complicated. Just as with the scalar
inverse, there are matrices that have no inverse. Unlike the scalar case, there are
a lot of matrices with no inverse. Also, computing the inverse
matrix is a lot more complicated than simply taking the
reciprocal of a value.Most common transformation matrices do have an inverse. And for the basic
transformation matrices, the inverse matrix is very easy to compute. For a pure
rotation matrix, simply compute a new rotation matrix by negating the angle that the
old one was generated with. For a translation matrix, negate the origin value in the
matrix. For a scale matrix, take the reciprocal of the scale along each axis.To take the inverse of a sequence of matrices, you can take the inverse of each of
the component matrices. But you have to do the matrix multiplication in
reverse order. So if we have M = TRS, then M-1 =
S-1R-1T-1.Quaternions, like matrices, have a multiplicative inverse. The inverse of a pure
rotation matrix, which quaternions represent, is a rotation about the same axis with
the negative of the angle. For any angle θ, it is the case that sin(-θ) = -sin(θ). It is also the case that cos(-θ) = cos(θ). Since the vector part of a quaternion is built by multiplying the
axis by the sine of the half-angle, and the scalar part is the cosine of the
half-angle, the inverse of any quaternion is just the negation of the vector
part.You can also infer this to mean that, if you negate the axis of rotation, you are
effectively rotating about the old axis but negating the angle. Which is true, since
the direction of the axis of rotation defines what direction the rotation angle
moves the points in. Negate the axis's direction, and you're rotating in the
opposite direction.In quaternion lingo, the inverse quaternion is more correctly called the
conjugate quaternion. We use the same inverse notation,
-1, to denote conjugate
quaternions.Incidentally, the identity quaternion is a quaternion who's rotation angle is
zero. The cosine of 0 is one, and the sine of 0 is zero, so the vector part of the
identity quaternion is zero and the scalar part is one.SolutionGiven our new knowledge of inverse matrices, we can solve our problem.C*N*O = R*C*OWe can right-multiply both sides of this equation by the inverse transform of
O.(C*N*O)*O-1 =
(R*C*O)*O-1C*N*I = R*C*IThe I is the identity transform. From here, we can left-multiply both sides by the
inverse transform of C:C-1 *(C*N) =
C-1*(R*C)N = C-1*(R*C)Therefore, given an offset that is in camera space, we can generate the
world-space equivalent by multiplying it between the camera and inverse camera
transforms.Transformation SpacesIt turns out that this is a generalized operation. It can be used for much more
than just orientation changes.Consider a scale operation. Scales apply along the main axes of their space. But
if you want to scale something along a different axis, how do you do that?You rotate the object into a coordinate system where the axis you want to scale
is one of the basis axes, perform your scale, then rotate
it back with the inverse of the previous rotation.Effectively, what we are doing is transforming, not positions, but other
transformation matrices into different spaces. A transformation matrix has some
input space and defines an output space. If we want to apply that transformation in
a different space, we perform this operation.The general form of this sequence is as follows. Suppose you have a transformation
matrix T, which operates on points in a space called F. We have some positions in
the space P. What we want is to create a matrix that applies T's transformation
operation, except that it needs to operate on points in the space of P. Given a
matrix M that transforms from P space to F space, that matrix is M-1*T*M.Final OrientationLet's look at how this all works out in code, with the Camera Relative tutorial. This works very similarly to the last
tutorial, but with a few differences.Since we are doing camera-relative rotation, we need to have an actual camera that
can move independently of the world. So we incorporate our camera code from our
world space into this one. As before, the I and K
keys will move the camera up and down, relative to a center point. The
J and K keys will move the camera left and
right around the center point. Holding Shift with these keys will
move the camera in smaller increments.The SpaceBar will toggle between three transforms: model-relative
(yaw/pitch/roll-style), world-relative, and camera-relative.Our scene also includes a ground plane as a reference.The display function only changed where needed to deal with
drawing the ground plane and to handle the camera. Either way, it's nothing that
hasn't been seen elsewhere.The substantive changes were in the OffsetOrientation
function:Camera Relative OffsetOrientationvoid OffsetOrientation(const glm::vec3 &_axis, float fAngDeg)
{
float fAngRad = Framework::DegToRad(fAngDeg);
glm::vec3 axis = glm::normalize(_axis);
axis = axis * sinf(fAngRad / 2.0f);
float scalar = cosf(fAngRad / 2.0f);
glm::fquat offset(scalar, axis.x, axis.y, axis.z);
switch(g_iOffset)
{
case MODEL_RELATIVE:
g_orientation = g_orientation * offset;
break;
case WORLD_RELATIVE:
g_orientation = offset * g_orientation;
break;
case CAMERA_RELATIVE:
{
const glm::vec3 &camPos = ResolveCamPosition();
const glm::mat4 &camMat = CalcLookAtMatrix(camPos, g_camTarget, glm::vec3(0.0f, 1.0f, 0.0f));
glm::fquat viewQuat = glm::quat_cast(camMat);
glm::fquat invViewQuat = glm::conjugate(viewQuat);
const glm::fquat &worldQuat = (invViewQuat * offset * viewQuat);
g_orientation = worldQuat * g_orientation;
}
break;
}
g_orientation = glm::normalize(g_orientation);
}The change here is the addition of the camera-relative condition. To do this in
quaternion math, we must first convert the world-to-camera matrix into a quaternion
representing that orientation. This is done here using
glm::quat_cast.The conjugate quaternion is computed with GLM. Then, we simply multiply them with
the offset to compute the world-space offset orientation. This gets left-multiplied
into the current orientation, and we're done.InterpolationA quaternion represents an orientation; it defines a coordinate system relative to
another. If we have two orientations, we can consider the orientation of the same object
represented in both coordinate systems.What if we want to generate an orientation that is halfway between them, for some
definition of halfway? Or even better, consider an arbitrary
interpolation between two orientations, so that we can watch an object move from one
orientation to another. This would allow us to see an object smoothly moving from one
orientation to another.This is one more trick we can play with quaternions that we can't with matrices.
Linearly-interpolating the components of matrices does not create anything that
resembles an inbetween transformation. However, linearly interpolating a pair of
quaternions does. As long as you normalize the results.The Interpolation tutorial demonstrates this. The
Q, W, E, R,
T, Y, and U keys cause the ship
to interpolate to a new orientation. Each key corresponds to a particular orientation,
and the Q key is the initial orientation.We can see that there are some pretty reasonable looking transitions. The transition
from Q to W, for example. However, there are some
other transitions that don't look so good; the Q to E
transition. What exactly is going on?The Long PathUnit quaternions represent orientations, but they are also vector directions.
Specifically, directions in a four-dimensional space. Being unit vectors, they
represent points on a 4D sphere of radius one. Therefore, the path between two
orientations can be considered to be simply moving from one direction to another on
the surface of the 4D sphere.While unit quaternions do represent orientations, a quaternion is not a
unique representation of an orientation. That is, there are
multiple quaternions that represent the same orientation. Well, there are
two.The conjugate of a quaternion, its inverse orientation, is the negation of the
vector part of the quaternion. If you negate all four components however, you get
something quite different: the same orientation as before. Negating a quaternion
does not affect its orientation.While the two quaternions represent the same orientation, they aren't the same as
far as interpolation is concerned. Consider a two-dimensional case:If the angle between the two quaternions is greater than 90°, then the
interpolation between them will take the long path between the two
orientations. Which is what we see in the Q to E
transition. The orientation R is the negation of
E; if you try to interpolate between them, nothing changes. The
Q to R transition looks much better
behaved.This can be detected easily enough. If the 4-vector dot product between the two
quaternions is less than zero, then the long path will be taken. If you want to
prevent the long path from being used, simply negate one of the quaternions before
interpolating if you detect this. Similarly, if you want to force the long path,
then ensure that the angle is greater than 90° by negating a quaternion if the dot
product is greater than zero.Interpolation SpeedThere is another problem. Notice how fast the Q to
E interpolation is. It starts off slow, then rapidly spins
around, then slows down towards the end. Why does this happen?The linear interpolation code looks like this:Quaternion Linear Interpolationglm::fquat Lerp(const glm::fquat &v0, const glm::fquat &v1, float alpha)
{
glm::vec4 start = Vectorize(v0);
glm::vec4 end = Vectorize(v1);
glm::vec4 interp = glm::mix(start, end, alpha);
interp = glm::normalize(interp);
return glm::fquat(interp.w, interp.x, interp.y, interp.z);
}GLM's quaternion support does something unusual. The W component is given
first to the fquat constructor. Be aware of that when looking
through the code.The Vectorize function simply takes a quaternion and returns
a vec4; this is necessary because GLM fquat don't support
many of the operations that GLM vec4's do. In this case, the
glm::mix function, which performs component-wise linear
interpolation.Each component of the vector is interpolated separately from the rest. The
quaternion for Q is (0.7071f, 0.7071f, 0.0f, 0.0f), while the
quaternion for E is (-0.4895f, -0.7892f, -0.3700f, -0.02514f). In
order for the first componet of Q to get to E's first component, it will have to go
through zero.When the alpha is around 0.5, half-way through the movement, the resultant vector
before normalization is very small. But the vector itself isn't what provides the
orientation; the direction of the 4D vector is. Which is why it
moves very fast in the middle: the direction is changing rapidly.In order to get smooth interpolation, we need to interpolate based on the
direction of the vectors. That is, we interpolate along the angle between the two
vectors. This kind of interpolation is called spherical linear
interpolation or slerp.To see the difference this makes, press the SpaceBar; this
toggles between regular linear interpolation and slerp. The slerp version is much
smoother.The code for slerp is rather complex:Spherical Linear Interpolationglm::fquat Slerp(const glm::fquat &v0, const glm::fquat &v1, float alpha)
{
float dot = glm::dot(v0, v1);
const float DOT_THRESHOLD = 0.9995f;
if (dot > DOT_THRESHOLD)
return Lerp(v0, v1, alpha);
glm::clamp(dot, -1.0f, 1.0f);
float theta_0 = acosf(dot);
float theta = theta_0*alpha;
glm::fquat v2 = v1 - v0*dot;
v2 = glm::normalize(v2);
return v0*cos(theta) + v2*sin(theta);
}Slerp and PerformanceIt's important to know what kind of problems slerp is intended to solve and
what kind it is not. Slerp becomes increasingly more important the more
disparate the two quaternions being interpolated are. If you know that two
quaternions are always quite close to one another, then slerp isn't worth the
expense.The acos call in the slerp code alone is pretty
substantial in terms of performance. Whereas lerp is typically just a
vector/scalar multiply followed by a vector/vector addition. Even on the CPU,
the performance difference is important, particularly if you're doing thousands
of these per frame. As you might be in an animation system.In ReviewIn this tutorial, you have learned the following:A fixed sequence of successive rotations can prevent other rotations from
contributing to the object's orientation.Quaternions are 4-dimensional vectors that can encode an orientation. They can
be used for successively applying small rotations to an orientation. Matrices
fail at this because of the difficulty of orthonormalizing them to avoid
floating-point error accumulation.Quaternions work almost identically to matrices, in so far as they specify
orientations. They can be constructed directly from an angle/axis rotation, and
they can be composed with one another via quaternion multiplication.One can transform a matrix, or a quaternion with another matrix or quaternion,
such that the resulting transform is specified in a different space. This is
useful for applying rotations to an object's orientation that are in
camera-space,, while the object's orientation remains a model-to-world
transform.Quaternions can be interpolated, either with component-wise linear
interpolation or with spherical linear interpolation. If the angle between two
quaternion vectors is greater than 90°, then the interpolation between them will
move indirectly between the two.Further StudyTry doing the following with the orientation tutorials.Modify the Interpolation tutorial to allow multiple animations to be
active simultaneously. The Orientation class is
already close to being able to allow this. Instead of storing a single
Orientation::Animation object, it should store a
std::deque of them. When the external code adds a
new one, it gets pushed onto the end of the deque. During update, the
front-most entries can end and be popped off, recording its destination
index as the new current one in the Orientation
class. To get the orientation, just call each animation's orientation
function, feeding the previous result into the next one.Change the Interpolation tutorial to allow one to specify whether the long
path or short path between two orientations should be taken. This can work
for both linear and spherical interpolation.Further ResearchThis discussion has focused on the utility of quaternions in orienting objects,
and it has deftly avoided answering the question of exactly what a quaternion
is. After all, saying that a quaternion is a
four-dimensional complex number does not explain why they are useful in graphics.
They are a quite fascinating subject for those who like oddball math things.This discussion has also glossed over a few uses of quaternions in graphics, such
as how to directly rotate a position or direction by a quaternion. Such information
is readily available online.Glossarygimbal lockWhen applying 3 or more successive rotations about axes that are
orthogonal to each other, gimbal lock occurs when a degree of rotational
freedom is lost due to two or more axes that cause the same effective
rotation.orthonormalA transform has the property of being orthonormal if the three basis axes
are all orthogonal and the three axes are normal (have a length of
1).quaternionA four-dimensional vector that represents an orientation. The first three
components of the quaternion, the X, Y and Z, are considered the vector part
of the quaternion. The fourth component is the scalar part.inverse matrixThe inverse matrix of the matrix M is the matrix N for which the following
equation is true: MN = I, where I is the identity matrix. The inverse of M is
usually denoted as M-1.conjugate quaternionAnalogous to the inverse matrix. It is computed by negating the vector
part of the quatenrion.spherical linear interpolation, slerpInterpolation between two unit vectors that is linear based on the angle
between them, rather than the vectors themselves.