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Finding Signed Angle Between Vectors
I'm in need of help with a little math issue.
So, I got a vector v, representing an orientation, and two points s and t. What I what to do, is to find the rotation to apply to my vector v, in order to make it parallel with the vector defined by the two given points.
currently I'm somewhat achieving this, that is, I'm able to find the angle, just not the way to apply it (clockwise or counter clockwise).
Currently I'm just calculating the acos to the dot product of the vectors.
Any input is welcome.
Let's say acos gives you a value between 0 and pi.
Let's also say the vector from s to t is called u. As you have already computed,
acos((v . u)/(|v| * |u|))
gives you an angle alpha. Now in truth, v could be u rotated by alpha to one or the other direction.
You probably need this in 2D, but I'll go on in 3D first.
The rotation should be around a vector that is perpendicular to both v and u. This vector is of course the cross product of the two: u x v
Let's see an example:
/ v
/
/\ alpha
/ )
------------ u
In this case, u x v gives a vector towards the outside of your monitor. At the same time, you can see that the ration alpha should take place counterclockwise to make v parallel to u.
That is, in 3D, you have to compute w = u x v and always rotate v by alpha counterclockwise with respect to w. Alternatively, you can rotate v by alpha clockwise with respect to -w (which is v x u).
In 2D, I assume you want to rotate around z and you don't know which direction. You can apply the same method as above:
Compute w = u x v
If w has positive z (the x and y will be zero)
then, v should be rotated counterclockwise.
else, v should be rotated clockwise.
Related
I am trying to programmatically visualise a vector point but I want to clarify my output result.
If a vector p = i = [1,0,0] rotate by 90 degree about the x-axis, then quaternion q is: q = cos(45) + [1,0,0]*sin(45) = 0.707 + 0.707*i.
pn = qpq-1;
Now calculate pn: (0.707+0.707*i)(i)(0.707-0.707*i) = i.
So, the rotated vector pn = [1,0,0]. Which is p=pn.
Is p=pn correct? If it is can anyone explain it? or is this a special property of quaternions?
In the example you provided, you basically rotate a vector around itself (i.e. the axis of rotation is equal to the rotated vector, in this case [1,0,0]). As said in the comments, rotating a vector around itself, leaves it intact, regardless of the rotation angle.
Try your example where the rotated vector is along the y-axis [0,1,0], and the rotation axis is [1,0,0]. Maybe this will help you visualize some basic rotations.
Also, be mindful that a rotation of vector v using a unit quaternion q is given by:
Imaginary{q * [0, v_x, v_y, v_z] * conjugate(q)}
I am not sure if a question like this was asked before but i searched and didn't found what i am looking for.
I know how to determine if a point is to the left or right of a 2D line. but suppose we have a vector in 3D. of course a 3D vector passes through infinite planes, but suppose we chose one plane of them in which we are interested, and we have a specific point on this plane which we want to know if it lies to the left or right or on our vector (with respect to the chosen plane). how to do this ?
You should explicitly define orientation of that plane - for example, define main (forward) normal N - like OZ axis is normal for OXY plane.
If you have A,B,C triangle and claim that it is oriented counterclockwise, you can calculate forward plane normal as N = AB x BC
For points A, B, D in given plane calculate mixed product (vector product of AB and AD, then scalar product of result and N)
mp = (AB x AD) . dot. N
Sign of this value is positive, if vectors AB, AD, N form right-handed triplet and D lies left to AB direction
An intuitive solution is to define a coordinate system for the plane as follows. Let's normalize the 3d vector in your question and call the resulting unit vector v, and let x be a point on your plane, whose unit normal we will denote as n. You can now chose a coordinate system centered at x, that is made by the three 3*1 unit vectors v, n and b=v.crossProduct(n).
The idea is that if you express a point in this coordinate system, then if its b coordinate is negative, you can says that it is, say, on the left. So, if its b coordinate is positive, it will be on the right.
Obviously, if you have a point q expressed in this coordinates system, you can write its expression q_w in world coordinates using
q_w=R*q+x
where the rotation matrix R is the matrix whose columns are the unit axes of the plane coordinate system:
R=[v n b]
So, if you have a point Q in world coordinates, using the inverse of the relation above, you compute transpose(R)*(Q-x), and look at whether the b coordinate is positive or negative.
I have two 3D vectors called A and B that both only have a 3D position. I know how to find the angle along the unit circle ranging from 0-360 degrees with the atan2 function by doing:
EDIT: (my atan2 function made no sense, now it should find the "y-angle" between 2 vectors):
toDegrees(atan2(A.x-B.x,A.z-B.z))+180
But that gives me the Y angle between the 2 vectors.
I need to find the X angle between them. It has to do with using the x, y and z position values. Not the x and z only, because that gives the Y angle between the two vectors.
I need the X angle, I know it sounds vague but I don't know how to explain. Maybe for example you have a camera in 3D space, if you look up or down than you rotate the x-axis. But now I need to get the "up/down" angle between the 2 vectors. If I rotate that 3D camera along the y-axis, the x-axis doens't change. So with the 2 vectors, no matter what the "y-angle" is between them, the x-angle between the 2 vectors wil stay the same if y-angle changes because it's the "up/down" angle, like in the camara.
Please help? I just need a line of math/pseudocode, or explanation. :)
atan2(crossproduct.length,scalarproduct)
The reason for using atan2 instead of arccos or arcsin is accuracy. arccos behaves very badly close to 0 degrees. Small computation errors in argument will lead to disproportionally big errors in result. arcsin has same problem close to 90 degrees.
Computing the altitude angle
OK, it might be I finally understood your comment below about the result being independent of the y angle, and about how it relates to the two vectors. It seems you are not really interested in two vectors and the angle between these two, but instead you're interested in the difference vector and the angle that one forms against the horizontal plane. In a horizontal coordinate system (often used in astronomy), that angle would be called “altitude” or “elevation”, as opposed to the “azimuth” you compute with the formula in your (edited) question. “altitude” closely relates to the “tilt” of your camera, whereas “azimuth” relates to “panning”.
We still have a 2D problem. One coordinate of the 2D vector is the y coordinate of the difference vector. The other coordinate is the length of the vector after projecting it on the horizontal plane, i.e. sqrt(x*x + z*z). The final solution would be
x = A.x - B.x
y = A.y - B.y
z = A.z - B.z
alt = toDegrees(atan2(y, sqrt(x*x + z*z)))
az = toDegrees(atan2(-x, -z))
The order (A - B as opposed to B - A) was chosen such that “A above B” yields a positive y and therefore a positive altitude, in accordance with your comment below. The minus signs in the azimuth computation above should replace the + 180 in the code from your question, except that the range now is [-180, 180] instead of your [0, 360]. Just to give you an alternative, choose whichever you prefer. In effect you compute the azimuth of B - A either way. The fact that you use a different order for these two angles might be somewhat confusing, so think about whether this really is what you want, or whether you want to reverse the sign of the altitude or change the azimuth by 180°.
Orthogonal projection
For reference, I'll include my original answer below, for those who are actually looking for the angle of rotation around some fixed x axis, the way the original question suggested.
If this x angle you mention in your question is indeed the angle of rotation around the x axis, as the camera example suggests, then you might want to think about it this way: set the x coordinate to zero, and you will end up with 2D vectors in the y-z plane. You can think of this as an orthogonal projection onto said plain. Now you are back to a 2D problem and can tackle it there.
Personally I'd simply call atan2 twice, once for each vector, and subtract the resulting angles:
toDegrees(atan2(A.z, A.y) - atan2(B.z, B.y))
The x=0 is implicit in the above formula simply because I only operate on y and z.
I haven't fully understood the logic behind your single atan2 call yet, but the fact that I have to think about it this long indicates that I wouldn't want to maintain it, at least not without a good explanatory comment.
I hope I understood your question correctly, and this is the thing you're looking for.
Just like 2D Vectors , you calculate their angle by solving cos of their Dot Product
You don't need atan, you always go for the dot product since its a fundamental operation of vectors and then use acos to get the angle.
double angleInDegrees = acos ( cos(theta) ) * 180.0 / PI;
I'm doing something where I have a plane in a coord sys A with a set of points already on it. I also have a normal vector in space N. How can I rotate the points on coord sys A so that the underlying plane will have the same normal direction as N?
Wondering if any one has a good idea on how to do this. Thanks
If you have, or can easily compute, the normal vector to the plane that your points are currently in, I think the easiest way to do this will be to rotate around the axis common to the two planes. Here's how I'd go about it:
Let M be the vector normal to your current plane, and N be the vector normal to the plane you want to rotate into. If M == N you can stop now and leave the original points unchanged.
Calculate the rotation angle as
costheta = dot(M,N)/(norm(M)*norm(N))
Calculate the rotation axis as
axis = unitcross(M, N)
where unitcross is a function that performs the cross product and normalizes it to a unit vector, i.e. unitcross(a, b) = cross(a, b) / norm(cross(a, b)). As user1318499 pointed out in a comment, this step can cause an error if M == N, unless your implementation of unitcross returns (0,0,0) when a == b.
Compute the rotation matrix from the axis and angle as
c = costheta
s = sqrt(1-c*c)
C = 1-c
rmat = matrix([ x*x*C+c x*y*C-z*s x*z*C+y*s ],
[ y*x*C+z*s y*y*C+c y*z*C-x*s ]
[ z*x*C-y*s z*y*C+x*s z*z*C+c ])
where x, y, and z are the components of axis. This formula is described on Wikipedia.
For each point, compute its corresponding point on the new plane as
newpoint = dot(rmat, point)
where the function dot performs matrix multiplication.
This is not unique, of course; as mentioned in peterk's answer, there are an infinite number of possible rotations you could make that would transform the plane normal to M into the plane normal to N. This corresponds to the fact that, after you take the steps described above, you can then rotate the plane around N, and your points will be in different places while staying in the same plane. (In other words, each rotation you can make that satisfies your conditions corresponds to doing the procedure described above followed by another rotation around N.) But if you don't care where in the plane your points wind up, I think this rotation around the common axis is the simplest way to just get the points into the plane you want them in.
If you don't have M, but you do have the coordinates of the points in your starting plane relative to an origin in that plane, you can compute the starting normal vector from two points' positions x1 and x2 as
M = cross(x1, x2)
(you can also use unitcross here but it doesn't make any difference). If you have the points' coordinates relative to an origin that is not in the plane, you can still do it, but you'll need three points' positions:
M = cross(x3-x1, x3-x2)
A single vector (your normal - N) will not be enough. You will need another two vectors for the other two dimensions. (Imagine that your 3D space could still rotate/spin around the normal vector, and you need another 2 vectors to nail it down). Once you have the normal and another one on the plane, the 3rd one should be easy to find (left- or right-handed depending on your system).
Make sure all three are normalized (length of 1) and put them in a matrix; use that matrix to transform any point in your 3D space (use matrix multiplication). This should give you the new coordinates.
I'm thinking make a unit vector [0,0,1] and use the dot-product along two planes to find the angle of difference, and shift all your points by those angles. This is assuming you want the z-axis to align with the normal vector, else just use [1,0,0] or [0,1,0] for x and y respectively.
Let's say I have two points in 3D space (a and b) and a fixed axis/unit vector called n.
I want to create a rotation matrix that minimizes the euclidan distance between point a (unrotated) and the rotated point b.
E.g:
Q := matrix_from_axis_and_angle (n, alpha);
find the unknown alpha that minimizes sqrt(|a - b*Q|)
Btw - If a solution/algorithm can be easier expressed with unit-quaternions go ahead and use them. I just used matrices to formulate my question because they're more widely used.
Oh - I know there are some degenerated cases ( a or b lying exactly in line with n ect.) These can be ignored. I'm just looking for the case where a single solution can be calculated.
sounds fairly easy. Assume unit vector n implies rotation around a line parallel to n through point x0. If x0 != the origin, translate the coordinate system by -x0 to get points a' and b' relative to new coordinate system origin 0, and use those 2 points instead of a and b.
1) calculate vector ry = n x a
2) calculate unit vector uy = unit vector in direction ry
3) calculate unit vector ux = uy x n
You now have a triplet of mutually perpendicular unit vectors ux, uy, and n, which form a right-handed coordinate system. It can be shown that:
a = dot(a,n) * n + dot(a,ux) * ux
This is because unit vector uy is parallel to ry which is perpendicular to both a and n. (from step 1)
4) Calculate components of b along unit vectors ux, uy. a's components are (ax,0) where ax = dot(a,ux). b's components are (bx,by) where bx = dot(b,ux), by = dot(b,uy). Because of the right-handed coordinate system, ax is always positive so you don't actually need to calculate it.
5) Calculate theta = atan2(by, bx).
Your rotation matrix is the one which rotates by angle -theta relative to coordinate system (ux,uy,n) around the n-axis.
This yields degenerate answers if a is parallel to n (steps 1 and 2) or if b is parallel to n (steps 4, 5).
I think you can rephrase the question to:
what is the distance from a point to a 2d circle in 3d space.
the answer can be found here
so the steps needed are as following:
rotating the point b around a vector n gives you a 2d circle in 3d space
using the above, find the distance to that circle (and the point on the circle)
the point on the circle is the rotated point b you are looking for.
deduce the rotated angle
...or something ;^)
The distance will be minimized when the vector from a to the line along n lines up with the vector from b to the line along n.
Project a and b into the plane perpendicular to n and solve the problem in 2 dimensions. The rotation you get there is the rotation you need to minimize the distance.
Let P be the plane that is perpendicular to n.
We can find the projection of a into the P-plane, (and similarly for b):
a' = a - (dot(a,n)) n
b' = b - (dot(b,n)) n
where dot(a,n) is the dot-product of a and n
a' and b' lie in the P-plane.
We've now reduced the problem to 2 dimensions. Yay!
The angle (of rotation) between a' and b' equals the angle (of rotation) needed to swing b around the n-axis so as to be closest to a. (Think about the shadows b would cast on the P-plane).
The angle between a' and b' is easy to find:
dot(a',b') = |a'| * |b'| * cos(theta)
Solve for theta.
Now you can find the rotation matrix given theta and n here:
http://en.wikipedia.org/wiki/Rotation_matrix
Jason S rightly points out that once you know theta, you must still decide to rotate b clockwise or counterclockwise about the n-axis.
The quantity, dot((a x b),n), will be a positive quantity if (a x b) lies in the same direction as n, and negative if (a x b) lies in the opposite direction. (It is never zero as long as neither a nor b is collinear with n.)
If (a x b) lies in the same direction as n, then b has to be rotated clockwise by the angle theta about the n-axis.
If (a x b) lies in the opposite direction, then b has to be rotated clockwise by the angle -theta about the n-axis.