Is there a way to determine what will be the destination lat/long if I have the initial lat/long, and distance from start point to the end point, and a slope of 98 degree with respect to the x-axis.
This link: http://www.movable-type.co.uk/scripts/latlong.html has the formulas required.
I am sorry that I can't find a pre-built script. Perhaps someeone else knows of a script that will apply those formulas for you. However, with a bit of effort, you should be able to take those formulas and create your own script. (It is a hassle - but the script may not be reasonably simple to write if you do decide you need to do it yourself.)
Related
I am creating a line from point shapefile which is auto generated. First time when I create that line in ArcGIS, I got a line like this because the points are not in a order:
after that I ordered the points according to it's location and got a line like this:
But unable to create a line like this:
Please give me any solution to fix this in ArcGIS or R programming. If you need the shapefile I can provide you.
I think there is no bullet proof way to restore the line, as same dataset can obviously represent different lines, so you would need to use some heuristics to do this. What Rafael described is very good top-bottom heuristics if you can reliably detect start and end points.
Another heuristics could be a bottom-up process to stitch nearby segments into a line. Find nearby points for every point, sort and connect the two nearest points. Continue this process, making sure each point has at most two connections, and that there are no loops.
A simpler approach that might just work if the line follows in general some direction is your idea of sorting points. But instead of ordering by x or y coordinate, find a linear approximation of these points, project each point to this straight line, and sort using the coordinate of the projection.
One way to go about this would be to treat it as a graph problem.
Create a weighted fully connected graph where the nodes are the points and the edge weight distance between its endpoints. Heuristically identify the “starting” and “ending” points of the line (for example, pick the bottom-leftmost point as start and top-rightmost and end).
Then you can use a shortest path algorithm to generate the order in which you connect the points.
Okay, so I have one OBJ file which I read into PCLpointcloud2. Now I want to feed it into a K-dTree. Which is not taking PCLPointCloud2 as input. I want to query any general point if it lies on the surface of my OBJ file.
I am finding it hard to understand their documentation. So how can it be done?
Plus, kindly point me to a good reference easily interpretable. And what is "PointT" BTW? Is it custom build type defined by us? please elaborate.
Look at the code in the provided tool pcl_mesh_sampling (in the PCL code directory under tools/mesh_sampling.cpp). It is relatively simple. It loads a model from PLY or OBJ then for each triangle it samples random points from the triangle. The final point cloud then undergoes a voxel-grid sample to make the points relatively uniform. Alternatively, you can just run the pcl_mesh_sampling program on your obj file to get an output PCD which you can then visualise with pcl_viewer before loading the PCD file into your own code.
Once you have the final point cloud, you can build and use a KD-Tree as per http://pointclouds.org/documentation/tutorials/kdtree_search.php
PointT is the template argument. The point cloud library can handle a variety of point types, from simple PointXYZ (having just x,y,z) to more complicated points like PointXYZRGBNormal (having x,y,z,normal_x,normal_y,normal_z, curvature, r, g, and b channels). Each algorithm is templated on the point type that you want to use. It would probably be easier if you used PointXYZ with your OBJ file, so use pcl::PointXYZ for all your template arguments. For more on templates see http://www.tutorialspoint.com/cplusplus/cpp_templates.htm and http://pointclouds.org/documentation/tutorials/adding_custom_ptype.php.
Update (reply to latest comment)
Added here because this reply is too long for a comment.
I think I see what you are getting at. So when you sample points from the point cloud & build a KD-tree of the object surface, and for each point you keep track which faces are nearby that point (probably all the faces adjacent to the face from which the point was sampled should be sufficient? Just one face is definitely insufficient). Then when the query point is given, you find the nearest point in the KD-tree and check whether the query point is on the "outside" or inside of the full list of nearby faces associated with that point in the KD-tree. If it's on the "inside" of all of them perhaps it is an interior point. But I cannot guarantee that this is true. That is my thinking on that question at the moment. But I do wonder if you want a mesh-based approach really. By the way, if you break your mesh up into convex parts then you can have nice guarantees when processing each convex part.
I get an organized point cloud (using pcl and an ASUS Xtion Pro Live), which of course contains NANs and the like. I also get an RGB image of the same scene.
The first step for processing is removing those NANs, which converts the point cloud to unorganized. I then perform a few other steps, but that's not relevant to the question (I think, see P.S.1). What COULD (I'm not sure) be relevant is that I run extract multiple times, and so have quite a few intermediate point clouds. I believe this means I can no longer assume that the points are in the same order they were at the start.
For clarification, I do understand what an unorganized point cloud it and how it differs from unorganized, both theoretically and in terms of how the data is actually stored.
After chopping off various points, I now have a much smaller point cloud which consists only of points in the original point cloud (but much less of them). How do I map these points back to the matching points in the original point cloud? I probably could iterate through the entire cloud to find matches, but this seems hacked together. Is there a better way to do this?
My main aim is to be able to say that 'point A in my final point cloud is of interest to me' and furthermore to map that to pixel K in the RGB image I first obtained. It seems to me that matching the final point cloud with the initial one is the best way to do this, but alternatives are also welcome.
P.S.1 - One of the last few steps in my process is finding a convex hull and then extracting a polygonal prism from the original point cloud. If all else fails, I will just interrogate the (20-50) points on the convex hull to match them with my initial point cloud (minimizing computation) and hence to match them with the original RGB images.
P.S.2 - Random musing - since I know the original size of the RGB image, the origin of the camera relative to the point cloud (or, rather, the position of the points relative to the camera used to take them), and can trivially obtain the camera parameters, could I simply use ray-tracing through each point in my final point cloud to produce an RGB image? The image may need registration with the 'real' RGB image, or it probably won't since nothing will have actually moved except for rounding error.
I'm maintaining software which uses PCL. I'm myself not much experienced in PCL, I've only tried some examples and tried to understand the official PCL-Ducumentation (which is unfortunately mainly sparse, doxygen-generated text). My impression is, only a PCL contributors have real change to use the library efficiently.
One feature I have to fix in the software is aligning two clouds. The clouds are two objects, which should be stacked together with a layer in-between (The actual task is to calculate the volume of the layer ).
I hope the picture explains the task well. The objects are scanned both from the sides to be stacked (one from above and the other from below). On both clouds the user selects manually two points. Then, as I hope there should be a mean in PCL to align two clouds providing the two clouds and the coordinates of the points. The alignment is required only in X-Y Plane.
Unfortunately I can't find out which function should I use for this, partly because the PCL documentation is IHMO really humble, partly because of lack of experience.
My desperate idea was to stack the clouds using P1 as the origin of both and then rotate the second cloud manually using the calculated angle between P11,P21 and P12,P22. This works, but since the task appears to me very common, I'd expect PCL to provide a dedicated function for that.
Could you point me to a proper API-function, code-snippet, example, similar project or a good book helping to understand PCL API and usage?
Many thanks!
I think this problem does not need PCL. It is simple enough to form the correct linear equation and solve it.
If you want to use PCL without worrying about the maths too much (though, if the above is a mystery to you, then studying some computational geometry would be very useful), here is my suggestion.
Most PCL operations work on 3D point clouds. I understand from your question that you only have 2D point clouds OR you don't care about the 3rd dimension. In this case if I were you I would represent the points as a 3D point cloud and set the z dimension to zero.
You will only need two point clouds with 3 points as that is how many points you are feeding to the transformation estimation algorithm. The first 2 points in the clouds will be the points chosen by the user. The third one will be any point that you have chosen that you know is the same in both clouds. You need this third one otherwise the transform is still ambiguous if it is a general transform that is being computed. You can calculate however such a point as you know 2 points already and you know that all the points are on a common plane (as you have projected them by losing the z values). Just don't choose it co-linear with the other two points. For example, halfway between the two points and 2cm in the perpendicular direction (ensuring to go in the correct direction).
Then you can use the estimateRigidTransformation functions to find the transform.
http://docs.pointclouds.org/1.7.0/classpcl_1_1registration_1_1_transformation_estimation_s_v_d.html
This function is also good for over-determined problems (it is the workhorse of the ICP algorithm in PCL) but as long as you have enough points to determine the transform it should work.
I am in charge of a program that is used to create a set of nodes and paths for consumption by an autonomous ground vehicle. The program keeps track of the locations of all items in its map by indicating the item's position as being x meters north and y meters east of an origin point of 0,0. In the real world, the vehicle knows the location of the origin's lat and long, as it is determined by a dgps system and is accurate down to a couple centimeters. My program is ignorant of any lat long coordinates.
It is one of my goals to modify the program to keep track of lat long coords of items in addition to an origin point and items' x,y position in relation to that origin. At first blush, it seems that I am going to modify the program to allow the lat long coords of the origin to be passed in, and after that I desire that the program will automatically calculate the lat long of every item currently in a map. From what I've researched so far, I believe that I will need to figure out the math behind converting to lat long coords from a UTM like projection where I specify the origin points and meridians etc as opposed to whatever is defined already for UTM.
I've come to ask of you GIS programmers, am I on the right track? It seems to me like there is so much to wrap ones head around, and I'm not sure if the answer isn't something as simple as, "oh yea theres a conversion from meters to lat long, here"
Currently, due to the nature of DGPS, the system really doesn't need to care about locations more than oh, what... 40 km? radius away from the origin. Given this, and the fact that I need to make sure that the error on my coordinates is not greater than .5 meters, do I need anything more complex than a simple lat/long to meters conversion constant?
I'm knee deep in materials here. I could use some pointers about what concepts to research.
Thanks much!
Given a start point in lat/long and a distance and bearing, finding the end point is a geodesic calculation. There's a great summary of geodesic calculations and errors on the proj.4 website. They come to the conclusion that using a spherical model can get results for distance between points with at most 0.51% error. That, combined with a formula to translate between WGS-84 and ECEF (see the "LLA to ECEF" and "ECEF to LLA" sections, seems like it gets you what you need.
If you want to really get the errors nailed down by inverse projecting your flat map to WGS-84, proj.4 is a projection software package. It has source code, and comes with three command line utilities - proj, which converts to/from cartographic projection and cartesian data; cs2cs, which converts between different cartographic projections; and geod, which calculates geodesic relationships.
The USGS publishes a very comprehensive treatment of map projections.
I'd do a full-up calculation if you can. That way you'll always be as accurate as you can be.
If you happen to be using C++ the GDAL is a very good library.
For a range of 40km, you may find that approximating the world to a 2D flat surface may work, although a UTM transform would be the ideal way to go - in any case, I'd advocate using the actual WGS84 co-ordinates & ellipsoid for calculations such as great circle distance, or calculating bearings.
If you get bored, you could go down a similar line to something I've been working on, that can be used as a base class for differing datums such as OSGB36 or WGS84...