Hello I'm looking for an algorithm that would let me add degrees for an specific coordinate. So let's suppose I have the following position -0.31399363,-78.44437. I have an image where I assume that this position is pointing north, now I want to be able to create another position that would have +30 degrees longitude offset, and is 100m farther from the initial point. I have been looking on internet, and I found some calculations in order to transforma Decimal coordinates to Degrees, I think that's the starting point, but I couldn't figure how to translate decimal coordinates according to distance.
Thanks a lot.
I'm not entirely certain I understand the question, but to determine distances and bearings between lat/long coordinates, you can use the Haversine formula. It sounds to me like that, or something that can be derived from it, is what you are looking for.
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I would like to find the best fit plane from a list of 3D points. It means the plane has the least square distance from all the points. I read the article
Best fit plane by minimizing orthogonal distances
and
3D Least Squares Plane
I fully understand the solutio but it turns other to be impractical in my situation. I need to read a very very large list of 3d points, direcltly impementation would result in ill posed problem. Even I subtract the data with their average,(refere to the document here-> part3 : http://www.geometrictools.com/Documentation/LeastSquaresFitting.pdf) the number is still very large. So what can I do?
Is there an iterative way to implement it ?
I have changed the way to ask the question, I hope may be there are someone can give me more advices on it ?
Given a list of 3D Points
{(x0,y0,z0),
(x1,y1,z1)...
(xn-1,yn-1,zn-1)}
I would like to construct a plane by fitting all the 3D points. In this sense, I mean to find the plane with format (Ax+By+Cy+D = 0), thus its uses four parameters(A,B,C,D) to characterize a plane. The sum of distance between each point and the plane should be minimium.
I do try the menthod provided in the below link
http://www.geometrictools.com/Documentation/LeastSquaresFitting.pdf
But there are two problems:
-During calculation, the above algorithm needs to do summation of all points value, which lead to overflow problem if my number of points increases
-given newly added points, it has to do all the calculation again, is there a way to use the before calculated plane parameter and the newly given points to somehow fine tune the planes parameters?
PS:I am a bit greedy, if we need to involve all the points, it is possible that the plane finally obtained isn't good enough.I am thinking of using random sample consensus(RANSAC), is it the right direction?
If you are expecting a plane then most of the points are not that useful since even a handful should give you a good approximation of the final solution (module a bit more noise).
So here's the solution. Sample down your data set to something that works and run the smaller set through the fitting algorithm.
If you are not expecting that the points are on a plane then sub-sampling should still work, but you must consider error ranges for any solution (since they will likely be fairly big).
It seems that there are lots of information both in Google and here, that speak a lot about many different conversions of latitude,longitude.
So what i'm asking is for you to be simple as possible, and try not sending me to other places to seek an answer.
I am trying to put the entire world in to 2D square, where each point represent the distance(in meters) from a point which I choose to define it (0,0),
Can you give me a mathematical algorithm to do so.
You could either use a azimuthal equidistant or two-point equidistant projection.
Of these the azimuthal equidistant is easiest. To do this, just start at your reference point on the world, and put this in the center of your map. Then proceed outward in concentric circles on the map, and for each new circle plot all the points on the world at the appropriate distance and angle.
After doing this, your map should look like a circle, and all of the points will be the correct distance from your center point.
Let's say I have a upright capsule shape (swept sphere) that I would like to cast it along a velocity vector. I would like to be able to find the point of contact and a surface normal for any convex shapes it would intersect along this path. I would also like to find the distance the swept caspule traveled to the point of first contact.
Heres a quick diagram of a capsule being casted against a large convex polyhedra (only one face is drawn)
What kind of algorithm or process could do this? I assume it would be similar to a sphere-cast, but i can't find much on that either.
Since you are considering capsules and convex polyhedra, I suppose you could use something based on GJK. You would get the point of contact and a surface normal during a collision, and the minimum distance between the objects and the associated witness points if there is no collision.
You can also take a look at this publication on Interactive and Continuous Collision Detection for Avatars in Virtual Environments.
Right if its the same as your diagram then finding where it collides is the easy part. Get the circles x and y coordinates and plus '+' the radius of the circle. If that point is at the line of the path then its a collision. The line will have to be found using the line equation here y = mx+c.
The distance can be calculated by setting an intial values of x and y. and then when the object hits set final variables to x and y again. then just the lenght of a line formula to calculate the distance travelled.
The problem is im going on what i know from C++ and i dont know what your programming in.
i think you wanted something else but cant work out what that is from paragraph.
I know how to implement n log n closest pair of points algorithm (Shamos and Hoey) for 2D cases (x and y). However for a problem where latitude and longitude are given this approach cannot be used. The distance between two points is calculated using the haversine formula.
I would like to know if there is some way to convert these latitudes and longitudes to their respective x and y coordinates and find the closest pair of points, or if there is another technique that can be used to do it.
I would translate them to three dimensional coordinates and then use the divide and conquer approach using a plane rather than a line. This will definitely work correctly. We can be assured of this because when only examining points on the sphere, the two closest points by arc distance (distance walking over the surface) will also be the two closest by 3-d Cartesian distance. This will have running time O(nlogn).
To translate to 3-d coordinates, the easiest way is to make (0,0,0) the center of the earth and then your coordinates are (cos(lat)*cos(lon),cos(lat)*sin(lan),sin(lat)). For those purposes I'm using a scale for which the radius of the Earth is 1 in order to simplify calculations. If you want distance in some other unit, just multiply all quantities by the radius of the Earth when measured in that unit.
I should note that all this assumes that the earth is a sphere. It's not exactly one and points may actually have altitude as well, so these answers won't really be completely exact, but they will be very close to correct in almost every case.
I have a point (Lat/Lon) and a heading in degrees (true north) for which this point is traveling along. I have numerous stationary polygons (Points defined in Lat/Lon) which may or may not be convex.
My question is, how do I calculate the closest intersection point, if any, with a polygon. I have seen several confusing posts about Ray Tracing but they seem to all relate to 3D when the Ray and Polygon are not on the same Plane and also the Polygons must be convex.
sounds like you should be able to do a simple 2d line intersection...
However I have worked with Lat/Long before and know that they aren't exactly true to any 2d coordinate system.
I would start with a general "IsPointInPolygon" function, you can find a million of them by googling, and then test it on your poly's to see how well it works. If they are accurate enough, just use that. But it is possible that due to the non-square nature of lat/long coordinates, you may have to do some modifications using Spherical geometry.
In 2D, the calculations are fairly simple...
You could always start by checking to make sure the ray's endpoint is not inside the polygon (since that's the intersection point in that case).
If the endpoint is out of the line, you could do a ray/line segment intersection with each of the boundary features of the polygon, and use the closest found location. That handles convex/concave features, etc.
Compute whether the ray intersects each line segment in the polygon using this technique.
The resulting scaling factor in (my accepted) answer (which I called h) is "How far along the ray is the intersection." You're looking for a value between 0 and 1.
If there are multiple intersection points, that's fine! If you want the "first," use the one with the smallest value of h.
The answer on this page seems to be the most accurate.
Question 1.E GodeGuru