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I have drawn a 2D representation of the problem, but I will eventually have to solve this in 3 dimensions.
A line is drawn from an origin point to infinity, in a direction given by pitch and yaw. There is an axis-aligned box "in front of" the point.
I want to get the coordinates of the point on box that is closest to the line, or, if it intersects, closest to the origin point.
I.e., if the line were 'turned' towards the box, which point of the box would intersect with the line first?
Make parametric represenation of the ray with base point P0, direction vector D and parameter t
P = P0 + t * D
Get t for intersections of the ray with rectangle edges like this (similar in 3d):
Rect.Right = X0 + t * D.X
Find what intersection occurs first (smaller t), check coordinates of intersection. If inside edge - point found. If not, analyze intersection parameters with edgr continuations to determine what corner (perhaps edge in 3d) is the closest
Note that in 2d case you need to check only two possible edges - depending on ray direction. For example - left and bottom for your right picture. When you see that intersections are out of edges - check what of two corners is closer. The same for 3d - but intersection is possible for three faces and closest for more edges or corners.
Let the position of the point be (x0,y0,z0) and the box have corners (x1,y1,z1) and (x2,y2,z2) with x1 < x2, y1 < y2, z1 < z2. In terms of yaw ψ and pitch θ a unit vector along the line will be give by
(u,v,w) = (cos ψ sin θ, sin ψ, cos ψ cos θ)
The line is (x0,y0,z0) + t (u,v,w)
Finding intersection with one of the plane containing a face of the box is trivial. Say to find the intersection with the plane x=x1, just requires solving
x0 + u t = x1 so t = (x1-x0)/u. Once found its easy to check if the intersection is contained in the face.
The tricky situation happens if the line does not intersect the faces. Here we have a pair of skew lines, and wish to find the closest pair of points one on each line.
Consider the closest point to the edge from (x1,y1,z1) to (x2,y1,z1).
We want to find the parameter s,t such that that the points
(x0,y0,z0)+s(u,v,w)
(x1,y1,z1)+t(1,0,0)
are the closest. The segment joining these points must be perpendicular to both lines. A vector along that line is the cross product
N = (u,v,w) X (1,0,0) = (0,w,-v)
Now consider the plane through (x1,y1,z1) spanned by (1,0,0) and N, this has normal
N2 = (1,0,0) X N
= (1,0,0) X (0,w,-v)
= (0,v,w)
and the plane is defined by
P . N2 = (x1,y1,z1) . N2
Take a point on our ray
( (x0,y0,z0)+s(u,v,w) ) . N2 = (x1,y1,z1) . N2
(x0,y0,z0) . N2 + s (u,v,w) . N2 = (x1,y1,z1) . N2
s (u,v,w) . N2 = ((x1,y1,z1)-(x0,y0,z0)) . N2
s (v^2+w^2) = (y1-y0) v + (z1-z0) w
so
s = [ (y1-y0) v + (z1-z0) w ] / (v^2+w^2)
We can repeat the above for each edge on the box, find the closest points and select the smallest.
x1,y1 is a point inside a circle (not in the circumference of the circle). How can I calculate the diametrically opposite point?
|
| x1,y1
|
-------|--------
|
x2,y2 |
|
Option 1: Convert it to polar coordinates, and add pi to the angular part.
You'd basically use atan2 (available in most languages) to compute the angle, and pythagoras formula to compute the radius.
Option 2: Compute the difference relative to origo, and add the negation of that to the origo point.
Let (ox, oy) be the center of the circle. Now the "opposite point" could be computed with
x2 = ox - (x1 - ox)
y2 = oy - (y1 - oy)
If you can assume that the center is at (0,0), why wouldn't you just take (-x1, -y1)? If it is anything diferent, add the -x1, -y1 to the center coordinates.
This sounds like a homework question. But I'll give the asker a break and say:
(x2,y2) = f(x1,y1) where f is (x * -1, y * -1).
If the center of the circle is at (0,0) then x2 = -x1, y2 = -y1. If the center is at (xc, yc), then x2 = 2 xc - x1, y2 = 2 yc - y1.
Goal
I want to determine if a test point is within a defined quadrilateral. I'm probably going to implement the solution in Matlab so I only need pseudo-code.
Inputs
Corners of quadrilateral : (x1,y1) (x2,y2) (x3,y3) (x4,y4)
Test point : (xt, yt)
Output
1 - If within quadrilateral
0 - Otherwise
Update
It was pointed out that identifying the vertices of the quadrilateral is not enough to uniquely identify it. You can assume that the order of the points determines the sides of the quadrilateral (point 1 connects 2, 2 connects to 3, 3 connects to 4, 4 connects to 1)
You can test the Point with this condition. Also you can treat quadrilateral as 2 triangles to calculate its area.
Use inpolygon. Usage would be inpolygon(xt,yt,[x1 x2 x3 x4],[y1 y2 y3 y4])
Since it's a simple quadrilateral you can test for a point in triangle for each end and a point in rectangle for the middle.
EDIT Here is some pseudo code for point in triangle:
function SameSide(p1,p2, a,b)
cp1 = CrossProduct(b-a, p1-a)
cp2 = CrossProduct(b-a, p2-a)
if DotProduct(cp1, cp2) >= 0 then return true
else return false
function PointInTriangle(p, a,b,c)
if SameSide(p,a, b,c) and SameSide(p,b, a,c)
and SameSide(p,c, a,b) then return true
else return false
Or using Barycentric technique:
A, B, and C are the triangle end points, P is the point under test
// Compute vectors
v0 = C - A
v1 = B - A
v2 = P - A
// Compute dot products
dot00 = dot(v0, v0)
dot01 = dot(v0, v1)
dot02 = dot(v0, v2)
dot11 = dot(v1, v1)
dot12 = dot(v1, v2)
// Compute barycentric coordinates
invDenom = 1 / (dot00 * dot11 - dot01 * dot01)
u = (dot11 * dot02 - dot01 * dot12) * invDenom
v = (dot00 * dot12 - dot01 * dot02) * invDenom
// Check if point is in triangle
return (u > 0) && (v > 0) && (u + v < 1)
If the aim is to code your own test, then pick any classic point in polygon test to implement. Otherwise do what Jacob suggests.
assuming you the given coordinates are arranged s.t.
(x1,y1) = rightmost coordinate
(x2,y2) = uppermost coordinate
(x3,y3) = leftmost coordinate
(x4,y4) = botoom-most coordinate
You can do the following:
1. calculate the 4 lines of the quadrilateral (we'll call these quad lines)
2. calculate 4 lines, from the (xt, yt) to every other coordinate (we'll call these new lines)
3. if any new line intersects any of the quad lines, then the coordinate is outside of the quadrilateral, otherwise it is inside.
Assume A,B,C,D are the vertices of the quadrilateral and P is the point.
If P is inside the quadrilateral then all dot products dot(BP,BA), dot(BP,BC), dot(AP,AB), dot(AP,AD), dot(DP,DC), dot(DP,DA), dot(CP,CB) and dot(CP,CD) will be positive.
If P is outside the quadrilateral at least one of these products will be negative.
The solution I used to solve this problem was to get the angle of P (in the diagrams the OP posted) for each of the 4 triangles it makes with each side of the quadrilateral. Add the angles together. If they equal (or nearly equal, depending on the error tolerance of the code) 360, the point is inside the quadrilateral. If the sum is less than 360, the point is outside. However, this might only work with convex quadrilaterals.
I have three vertices which make up a plane/polygon in 3D Space, v0, v1 & v2.
To calculate barycentric co-ordinates for a 3D point upon this plane I must first project both the plane and point into 2D space.
After trawling the web I have a good understanding of how to calculate barycentric co-ordinates in 2D space, but I am stuck at finding the best way to project my 3D points into a suitable 2D plane.
It was suggested to me that the best way to achieve this was to "drop the axis with the smallest projection". Without testing the area of the polygon formed when projected on each world axis (xy, yz, xz) how can I determine which projection is best (has the largest area), and therefore is most suitable for calculating the most accurate barycentric co-ordinate?
Example of computation of barycentric coordinates in 3D space as requested by the OP. Given:
3D points v0, v1, v2 that define the triangle
3D point p that lies on the plane defined by v0, v1 and v2 and inside the triangle spanned by the same points.
"x" denotes the cross product between two 3D vectors.
"len" denotes the length of a 3D vector.
"u", "v", "w" are the barycentric coordinates belonging to v0, v1 and v2 respectively.
triArea = len((v1 - v0) x (v2 - v0)) * 0.5
u = ( len((v1 - p ) x (v2 - p )) * 0.5 ) / triArea
v = ( len((v0 - p ) x (v2 - p )) * 0.5 ) / triArea
w = ( len((v0 - p ) x (v1 - p )) * 0.5 ) / triArea
=> p == u * v0 + v * v1 + w * v2
The cross product is defined like this:
v0 x v1 := { v0.y * v1.z - v0.z * v1.y,
v0.z * v1.x - v0.x * v1.z,
v0.x * v1.y - v0.y * v1.x }
WARNING - Almost every thing I know about using barycentric coordinates, and using matrices to solve linear equations, was learned last night because I found this question so interesting. So the following may be wrong, wrong, wrong - but some test values I have put in do seem to work.
Guys and girls, please feel free to rip this apart if I screwed up completely - but here goes.
Finding barycentric coords in 3D space (with a little help from Wikipedia)
Given:
v0 = (x0, y0, z0)
v1 = (x1, y1, z1)
v2 = (x2, y2, z2)
p = (xp, yp, zp)
Find the barycentric coordinates:
b0, b1, b2 of point p relative to the triangle defined by v0, v1 and v2
Knowing that:
xp = b0*x0 + b1*x1 + b2*x2
yp = b0*y0 + b1*y1 + b2*y2
zp = b0*z0 + b1*z1 + b2*z2
Which can be written as
[xp] [x0] [x1] [x2]
[yp] = b0*[y0] + b1*[y1] + b2*[y2]
[zp] [z0] [z1] [z2]
or
[xp] [x0 x1 x2] [b0]
[yp] = [y0 y1 y2] . [b1]
[zp] [z0 z1 z2] [b2]
re-arranged as
-1
[b0] [x0 x1 x2] [xp]
[b1] = [y0 y1 y2] . [yp]
[b2] [z0 z1 z2] [zp]
the determinant of the 3x3 matrix is:
det = x0(y1*z2 - y2*z1) + x1(y2*z0 - z2*y0) + x2(y0*z1 - y1*z0)
its adjoint is
[y1*z2-y2*z1 x2*z1-x1*z2 x1*y2-x2*y1]
[y2*z0-y0*z2 x0*z2-x2*z0 x2*y0-x0*y2]
[y0*z1-y1*z0 x1*z0-x0*z1 x0*y1-x1*y0]
giving:
[b0] [y1*z2-y2*z1 x2*z1-x1*z2 x1*y2-x2*y1] [xp]
[b1] = ( [y2*z0-y0*z2 x0*z2-x2*z0 x2*y0-x0*y2] . [yp] ) / det
[b2] [y0*z1-y1*z0 x1*z0-x0*z1 x0*y1-x1*y0] [zp]
If you need to test a number of points against the triangle, stop here. Calculate the above 3x3 matrix once for the triangle (dividing it by the determinant as well), and then dot product that result to each point to get the barycentric coords for each point.
If you are only doing it once per triangle, then here is the above multiplied out (courtesy of Maxima):
b0 = ((x1*y2-x2*y1)*zp+xp*(y1*z2-y2*z1)+yp*(x2*z1-x1*z2)) / det
b1 = ((x2*y0-x0*y2)*zp+xp*(y2*z0-y0*z2)+yp*(x0*z2-x2*z0)) / det
b2 = ((x0*y1-x1*y0)*zp+xp*(y0*z1-y1*z0)+yp*(x1*z0-x0*z1)) / det
That's quite a few additions, subtractions and multiplications - three divisions - but no sqrts or trig functions. It obviously does take longer than the pure 2D calcs, but depending on the complexity of your projection heuristics and calcs, this might end up being the fastest route.
As I mentioned - I have no idea what I'm talking about - but maybe this will work, or maybe someone else can come along and correct it.
Update: Disregard, this approach does not work in all cases
I think I have found a valid solution to this problem.
NB: I require a projection to 2D space rather than working with 3D Barycentric co-ordinates as I am challenged to make the most efficient algorithm possible. The additional overhead incurred by finding a suitable projection plane should still be smaller than the overhead incurred when using more complex operations such as sqrt or sin() cos() functions (I guess I could use lookup tables for sin/cos but this would increase the memory footprint and defeats the purpose of this assignment).
My first attempts found the delta between the min/max values on each axis of the polygon, then eliminated the axis with the smallest delta. However, as suggested by #PeterTaylor there are cases where dropping the axis with the smallest delta, can yeild a straight line rather than a triangle when projected into 2D space. THIS IS BAD.
Therefore my revised solution is as follows...
Find each sub delta on each axis for the polygon { abs(v1.x-v0.x), abs(v2.x-v1.x), abs(v0.x-v2.x) }, this results in 3 scalar values per axis.
Next, multiply these scaler values to compute a score. Repeat this, calculating a score for each axis. (This way any 0 deltas force the score to 0, automatically eliminating this axis, avoiding triangle degeneration)
Eliminate the axis with the lowest score to form the projection, e.g. If the lowest score is in the x-axis, project onto the y-z plane.
I have not had time to unit test this approach but after preliminary tests it seems to work rather well. I would be eager to know if this is in-fact the best approach?
After much discussion there is actually a pretty simple way to solve the original problem of knowing which axis to drop when projecting to 2D space. The answer is described in 3D Math Primer for Graphics and Game Development as follows...
"A solution to this dilemma is to
choose the plane of projection so as
to maximize the area of the projected
triangle. This can be done by
examining the plane normal; the
coordinate that has the largest
absolute value is the coordinate that
we will discard. For example, if the
normal is [–1, 0, 0], then we would
discard the x values of the vertices
and p, projecting onto the yz plane."
My original solution which involved computing a score per axis (using sub deltas) is flawed as it is possible to generate a zero score for all three axis, in which case the axis to drop remains undetermined.
Using the normal of the collision plane (which can be precomputed for efficiency) to determine which axis to drop when projecting into 2D is therefore the best approach.
To project a point p onto the plane defined by the vertices v0, v1 & v2 you must calculate a rotation matrix. Let us call the projected point pd
e1 = v1-v0
e2 = v2-v0
r = normalise(e1)
n = normalise(cross(e1,e2))
u = normalise(n X r)
temp = p-v0
pd.x = dot(temp, r)
pd.y = dot(temp, u)
pd.z = dot(temp, n)
Now pd can be projected onto the plane by setting pd.z=0
Also pd.z is the distance between the point and the plane defined by the 3 triangles. i.e. if the projected point lies within the triangle, pd.z is the distance to the triangle.
Another point to note above is that after rotation and projection onto this plane, the vertex v0 lies is at the origin and v1 lies along the x axis.
HTH
I'm not sure that the suggestion is actually the best one. It's not too hard to project to the plane containing the triangle. I assume here that p is actually in that plane.
Let d1 = sqrt((v1-v0).(v1-v0)) - i.e. the distance v0-v1.
Similarly let d2 = sqrt((v2-v0).(v2-v0))
v0 -> (0,0)
v1 -> (d1, 0)
What about v2? Well, you know the distance v0-v2 = d2. All you need is the angle v1-v0-v2. (v1-v0).(v2-v0) = d1 d2 cos(theta). Wlog you can take v2 as having positive y.
Then apply a similar process to p, with one exception: you can't necessarily take it as having positive y. Instead you can check whether it has the same sign of y as v2 by taking the sign of (v1-v0)x(v2-v0) . (v1-v0)x(p-v0).
As an alternative solution, you could use a linear algebra solver on the matrix equation for the tetrahedral case, taking as the fourth vertex of the tetrahedron v0 + (v1-v0)x(v2-v0) and normalising if necessary.
You shouldn't need to determine the optimal area to find a decent projection.
It's not strictly necessary to find the "best" projection at all, just one that's good enough, and that doesn't degenerate to a line when projected into 2D.
EDIT - algorithm deleted due to degenerate case I hadn't thought of
This is not a homework. I am asking to see if problem is classical (trivial) or non-trivial. It looks simple on a surface, and I hope it is truly a simple problem.
Have N points (N >= 2) with
coordinates Xn, Yn on a surface of
2D solid body.
Solid body has some small rotation (below Pi/180)
combined with small shifts (below 1% of distance between any 2 points of N). Possibly some small deformation too (<<0.001%)
Same N points have new coordinates named XXn, YYn
Calculate with best approximation the location of center of rotation as point C with coordinates XXX, YYY.
Thank you
If you know correspondence (i.e. you know which points are the same before and after the transformation), and you choose to allow scaling, then the problem is a set of linear equations. If you have 2 or more points then you can find a least-squares solution with little difficulty.
For initial points (xi,yi) and transformed points (xi',yi') you have equations of the form
xi' = a xi + b yi + c
yi' =-b xi + a yi + d
which you can rearrange into a linear system
A x = y
where
A = | x1 y1 1 0 |
| y1 -x1 0 1 |
| x2 y2 1 0 |
| y2 -x2 0 1 |
| ... |
| xn yn 1 0 |
| yn -xn 0 1 |
x = | a |
| b |
| c |
| d |
y = | x1' |
| y1' |
| x2' |
| y2' |
| ... |
| xn' |
| yn' |
the standard "least-squares" form of which is
A^T A x = A^T y
and has the solution
x = (A^T A)^-1 A^T y
with A^T as the transpose of A and A^-1 as the inverse of A. Normally you would use an SVD or QR decomposition to compute the solution as they ought to be more stable and less computationally intensive than the inverse.
Once you've found x (and so the four elements of the transformation a, b, c and d) then the various elements of the transformation are given by
scale = sqrt(a*a+b*b)
rotation = atan2(b,a)
translation = (c,d)/scale
If you don't include scaling then the system is non-linear, and requires an iterative solution (but isn't too difficult to solve). If you do not know correspondence then the problem is substantially harder, for small transformations something like iterated closest point works, for large transformations it's a lot harder.
Edit: I forgot to include the centre of rotation. A rotation theta about an arbitrary point p is a sequence
translate(p) rotate(theta) translate(-p)
if you expand it all out as an affine transformation (essentially what we have above) then the translation terms come to
dx = px - cos(theta)*px + sin(theta)*py
dy = py - sin(theta)*px - cos(theta)*py
we know theta (rotation), dx (c) and dy (d) from the equations above. With a little bit of fiddling we can solve for px and py
px = 0.5*(dx - sin(theta)*dy/(1-cos(theta)))
py = 0.5*(dy + sin(theta)*dx/(1-cos(theta)))
You'll notice that the equations are undefined if theta is zero, because there is no centre of rotation when no rotation is performed.
I think I have all that correct, but I don't have time to double check it all right now.
Look up the "Kabsch Algorithm". It is a general-purpose algorithm for creating rotation matrices using N known pairs. Kabsch created it to assist denoising stereo photographs. You rotate a feature in picture A to picture B, and if it is not in the target position, the feature is noise.
http://en.wikipedia.org/wiki/Kabsch_algorithm
See On calculating the finite centre of rotation for
rigid planar motion for a relatively simple solution. I say "relatively simple" because it still uses things like psuedo-inverses and SVD (singular value decomposition). And here's a wikipedia article on Instant centre of rotation. And another paper: ESTIMATION OF THE FINITE CENTER OF ROTATION IN PLANAR MOVEMENTS.
If you can handle stiffer stuff, try Least Squares Estimation of Transformation Parameters Between Two Point Patterns.
First of all, the problem is non-trivial.
A "simple" solition. It works best when the polygon resembles circle, and points are distributed evenly.
iterate through N
For both old and new dataset, find the 2 farthest points of the point N.
So now you have the triangle before and after the transformation. Use the clockwise direction from the center of each triangle to number its vertices as [0] (=the N-th point in the original dataset), [1], and [2] (the 2 farthest points).
Calculate center of rotation, and deformation (both x and y) of this triangle. If the deformation is more then your 0.001% - drop the data for this triangle, otherwise save it.
Calculate the average for the centers of rotation.
The right solution: define the function Err(Point BEFORE[N], Point AFTER[N], double TFORM[3][3]), where BEFORE - constant old data points, AFTER - constant new data points, TFORM[3][3] affine transformation matrix, Err(...) function that returns the scalar error value, 0.0 when the TFORM translated BEFORE to exact AFTER, or some >0.0 error value. Then use any numeric math you want to find the minimum of the Err(TFORM): e.g. gradient search.
Calculate polygon centers O1 and O2. Determine line formulae for O1 with (X0, Y0) and O2 with (XX0, YY0). Find intersection of lines to get C.
If I understand your problem correctly, this could be solved in this way:
find extremities (furthest points, probably on several axises)
scale either one to match
their rotation should now be trivial (?)
Choose any 2 points on the body, P1, P2, before and after rotation. Find vectors between these before and after points. Cross these vectors with a vector normal to the plane of rotation. This results in two new vectors, the intersection of the lines formed by the initial points and these two new vectors is the center of the rotation.
{
if P1after = P1before return P1after
if P2after = P2before return P2after
Vector V1 = P1after - P1before
Vector V2 = P2after - P2before
normal = Vn // can be messy to create for arbitrary 3d orientation but is simple if you know orientation, for instance, normal = (0,0,1) for an object in the x,y plane)
Vector VL1 = V1 x Vn //Vector V1 cross product with Vn
Vector VL2 = V2 x Vn
return intersectLines(P1after,VL1,P2after,VL2) //Center of rotation is intersection of two lines
}
intersectLines(Point P1, Vector V1, Point P2, Vector V2)
{
//intersect two lines using point, direction form of a line
//returns a Point
}