Looking for an algorithm to compute actual distance from a latitude/longitude/elevation to the visible horizon taking into account the actual surrounding terrain and the curve of the earth. Assume you have enough terrain data for the surrounding several hundred miles from any of the open elevation datasets. The problem can be simplified to an approximate by checking a few cardinal directions. Ideally I'd like to be able to compute the real solution as well.
Disclosure: I'm the developer and maintainer of the below mentioned software package.
I'm not sure if you're still looking for a solution as this question is already a bit older. However, one solution for your problem would be to apply the open-source package HORAYZON (https://github.com/ChristianSteger/HORAYZON). It's based on the high-performance ray tracing library Intel Embree (https://www.embree.org) and thus very fast and it considers Earth's curvature. With this package, you can compute the horizon angle and the distance to the horizon line for one or multiple arbitrary location(s) on a Digital Elevation Model (DEM) and set various options like the number of cardinal sampling directions, the maximal search distance for the horizon, etc. However - I'm not sure what you mean by "real solution". Do you mean the "perfect" solution - i.e. by considering elevation information from all DEM cells without doing a discrete sampling along the azimuth angle? Unfortunately, this cannot be done with the above mentioned package (but one could theoretically implement it).
Related
I have a set of (slightly noisy) GPS coordinates that I want to turn into a path. How do I go about this?
I find it similar to this question, except my points are ordered. Also, the path does not need to go through the points, but just follow their general direction.
It seems that Bezier curves might be the answer, but is there a way to use Bezier curves on hundreds of points?
Q&A:
How are your points ordered They are ordered by time and attached to a travelling car. There might be data that specify that the the car is travelling backwards, but I can remove that data by requiring that all points move in a "forward" direction. So then I should have just a list of points that all go forwards in space and don't intersect with themselves.
What if we connect all the lines with straight lines It won't look pretty. I'd like for the lines to be continuous and curvy.
What about using a spline between all the points This too will make too much noise. The path will be very "jumpy". It would be better if we didn't care about going through points, but just near them.
It is a bit of heavy machinery, but you can model your GPS observations as points following a Gaussian process with Gaussian noise, where the main Gaussian process model specifies that the underlying unknown true x and y coordinates of two measurements close in time should be close, and the noise allows the observed x and y GPS measurement values to deviate a bit from the true x and y values predicted by the Gaussian process model. You can read the book "Gaussian Processes for Machine Learning" available online if you're interested. I think it's a really elegant, flexible and powerful solution, but it would take way too much space to explain it in enough detail here so you really do need to read about it from the book.
Once you've learned the most likely Gaussian process model solution, you can make predictions of x and y locations for any time point, and it will be a smooth curve, which you can then plot. It won't pass through the observed GPS locations exactly.
Hy!
I have organized point clouds from a Kinect sensor. Let's say I have a organized point cloud of a sofa with a table in front. What I would like the get are two clouds: sofa and table
I am searching for some algorithm to get the connected components.
Does anyone have some pseudo code or papers? Or maybe some code (Matlab)
My idea at the moment: I could use the 2D information to get neighboring pixels of a point.
Next I could check the euclidean distance to the neighboring pixels. If the distance is below a threshold, the pixel belongs to the same cluster. ...
Thanks
As #Amro pointed out, DBSCAN is the algorithm you should study. It is a clustering based on ''density-connected'' components.
Also note the GDBSCAN variant (Generalized DBSCAN). You are not restricted to primitive distances such as Euclidean, but you can make your "neighborhood" definition as complex as you'd like.
Matlab probably is not the best choice. For DBSCAN to be really fast, you need support for index acceleration. Recent scikit-learn (0.14 to be precise) just got basic index acceleration for DBSCAN, and ELKI has had it for years. ELKI seems to be more ''flexible'' wrt. to having GDBSCAN and having index structures that are easy to extend with custom distance functions. sklearn probably only accelerates a few built-in distances.
you can use the connected component segmentation plugin from "Tools>Segmentation>label connected component" from cloudcompare software
I have set of binary images, on which i need to find the cross (examples attached). I use findcontours to extract borders from the binary image. But i can't understand how can i determine is this shape (border) cross or not? Maybe opencv has some built-in methods, which could help to solve this problem. I thought to solve this problem using Machine learning, but i think there is a simpler way to do this. Thanks!
Viola-Jones object detection could be a good start. Though the main usage of the algorithm (AFAIK) is face detection, it was actually designed for any object detection, such as your cross.
The algorithm is Machine-Learning based algorithm (so, you will need a set of classified "crosses" and a set of classified "not crosses"), and you will need to identify the significant "features" (patterns) that will help the algorithm recognize crosses.
The algorithm is implemented in OpenCV as cvHaarDetectObjects()
From the original image, lets say you've extracted sets of polygons that could potentially be your cross. Assuming that all of the cross is visible, to the extent that all edges can be distinguished as having a length, you could try the following.
Reject all polygons that did not have exactly 12 vertices required to
form your polygon.
Re-order the vertices such that the shortest edge length is first.
Create a best fit perspective transformation that maps your vertices onto a cross of uniform size
Examine the residuals generated by using this transformation to project your cross back onto the uniform cross, where the residual for any given point is the distance between the projected point and the corresponding uniform point.
If all the residuals are within your defined tolerance, you've found a cross.
Note that this works primarily due to the simplicity of the geometric shape you're searching for. Your contours will also need to have noise removed for this to work, e.g. each line within the cross needs to be converted to a single simple line.
Depending on your requirements, you could try some local feature detector like SIFT or SURF. Check OpenSURF which is an interesting implementation of the latter.
after some days of struggle, i came to a conclusion that the only robust way here is to use SVM + HOG. That's all.
You could erode each blob and analyze their number of pixels is going down. No mater the rotation scaling of the crosses they should always go down with the same ratio, excepted when you're closing down on the remaining center. Again, when the blob is small enough you should expect it to be in the center of the original blob. You won't need any machine learning algorithm or training data to resolve this.
I am hacking a vacuum cleaner robot to control it with a microcontroller (Arduino). I want to make it more efficient when cleaning a room. For now, it just go straight and turn when it hits something.
But I have trouble finding the best algorithm or method to use to know its position in the room. I am looking for an idea that stays cheap (less than $100) and not to complex (one that don't require a PhD thesis in computer vision). I can add some discrete markers in the room if necessary.
Right now, my robot has:
One webcam
Three proximity sensors (around 1 meter range)
Compass (no used for now)
Wi-Fi
Its speed can vary if the battery is full or nearly empty
A netbook Eee PC is embedded on the robot
Do you have any idea for doing this? Does any standard method exist for these kind of problems?
Note: if this question belongs on another website, please move it, I couldn't find a better place than Stack Overflow.
The problem of figuring out a robot's position in its environment is called localization. Computer science researchers have been trying to solve this problem for many years, with limited success. One problem is that you need reasonably good sensory input to figure out where you are, and sensory input from webcams (i.e. computer vision) is far from a solved problem.
If that didn't scare you off: one of the approaches to localization that I find easiest to understand is particle filtering. The idea goes something like this:
You keep track of a bunch of particles, each of which represents one possible location in the environment.
Each particle also has an associated probability that tells you how confident you are that the particle really represents your true location in the environment.
When you start off, all of these particles might be distributed uniformly throughout your environment and be given equal probabilities. Here the robot is gray and the particles are green.
When your robot moves, you move each particle. You might also degrade each particle's probability to represent the uncertainty in how the motors actually move the robot.
When your robot observes something (e.g. a landmark seen with the webcam, a wifi signal, etc.) you can increase the probability of particles that agree with that observation.
You might also want to periodically replace the lowest-probability particles with new particles based on observations.
To decide where the robot actually is, you can either use the particle with the highest probability, the highest-probability cluster, the weighted average of all particles, etc.
If you search around a bit, you'll find plenty of examples: e.g. a video of a robot using particle filtering to determine its location in a small room.
Particle filtering is nice because it's pretty easy to understand. That makes implementing and tweaking it a little less difficult. There are other similar techniques (like Kalman filters) that are arguably more theoretically sound but can be harder to get your head around.
A QR Code poster in each room would not only make an interesting Modern art piece, but would be relatively easy to spot with the camera!
If you can place some markers in the room, using the camera could be an option. If 2 known markers have an angular displacement (left to right) then the camera and the markers lie on a circle whose radius is related to the measured angle between the markers. I don't recall the formula right off, but the arc segment (on that circle) between the markers will be twice the angle you see. If you have the markers at known height and the camera is at a fixed angle of inclination, you can compute the distance to the markers. Either of these methods alone can nail down your position given enough markers. Using both will help do it with fewer markers.
Unfortunately, those methods are imperfect due to measurement errors. You get around this by using a Kalman estimator to incorporate multiple noisy measurements to arrive at a good position estimate - you can then feed in some dead reckoning information (which is also imperfect) to refine it further. This part is goes pretty deep into math, but I'd say it's a requirement to do a great job at what you're attempting. You can do OK without it, but if you want an optimal solution (in terms of best position estimate for given input) there is no better way. If you actually want a career in autonomous robotics, this will play large in your future. (
Once you can determine your position you can cover the room in any pattern you'd like. Keep using the bump sensor to help construct a map of obstacles and then you'll need to devise a way to scan incorporating the obstacles.
Not sure if you've got the math background yet, but here is the book:
http://books.google.com/books/about/Applied_optimal_estimation.html?id=KlFrn8lpPP0C
This doesn't replace the accepted answer (which is great, thanks!) but I might recommend getting a Kinect and use that instead of your webcam, either through Microsoft's recently released official drivers or using the hacked drivers if your EeePC doesn't have Windows 7 (presumably it does not).
That way the positioning will be improved by the 3D vision. Observing landmarks will now tell you how far away the landmark is, and not just where in the visual field that landmark is located.
Regardless, the accepted answer doesn't really address how to pick out landmarks in the visual field, and simply assumes that you can. While the Kinect drivers may already have feature detection included (I'm not sure) you can also use OpenCV for detecting features in the image.
One solution would be to use a strategy similar to "flood fill" (wikipedia). To get the controller to accurately perform sweeps, it needs a sense of distance. You can calibrate your bot using the proximity sensors: e.g. run motor for 1 sec = xx change in proximity. With that info, you can move your bot for an exact distance, and continue sweeping the room using flood fill.
Assuming you are not looking for a generalised solution, you may actually know the room's shape, size, potential obstacle locations, etc. When the bot exists the factory there is no info about its future operating environment, which kind of forces it to be inefficient from the outset.
If that's you case, you can hardcode that info, and then use basic measurements (ie. rotary encoders on wheels + compass) to precisely figure out its location in the room/house. No need for wifi triangulation or crazy sensor setups in my opinion. At least for a start.
Ever considered GPS? Every position on earth has a unique GPS coordinates - with resolution of 1 to 3 metres, and doing differential GPS you can go down to sub-10 cm range - more info here:
http://en.wikipedia.org/wiki/Global_Positioning_System
And Arduino does have lots of options of GPS-modules:
http://www.arduino.cc/playground/Tutorials/GPS
After you have collected all the key coordinates points of the house, you can then write the routine for the arduino to move the robot from point to point (as collected above) - assuming it will do all those obstacles avoidance stuff.
More information can be found here:
http://www.google.com/search?q=GPS+localization+robots&num=100
And inside the list I found this - specifically for your case: Arduino + GPS + localization:
http://www.youtube.com/watch?v=u7evnfTAVyM
I was thinking about this problem too. But I don't understand why you can't just triangulate? Have two or three beacons (e.g. IR LEDs of different frequencies) and a IR rotating sensor 'eye' on a servo. You could then get an almost constant fix on your position. I expect the accuracy would be in low cm range and it would be cheap. You can then map anything you bump into easily.
Maybe you could also use any interruption in the beacon beams to plot objects that are quite far from the robot too.
You have a camera you said ? Did you consider looking at the ceiling ? There is little chance that two rooms have identical dimensions, so you can identify in which room you are, position in the room can be computed from angular distance to the borders of the ceiling and direction can probably be extracted by the position of doors.
This will require some image processing but the vacuum cleaner moving slowly to be efficiently cleaning will have enough time to compute.
Good luck !
Use Ultra Sonic Sensor HC-SR04 or similar.
As above told sense the walls distance from robot with sensors and room part with QR code.
When your are near to a wall turn 90 degree and move as width of your robot and again turn 90deg( i.e. 90 deg left turn) and again move your robot I think it will help :)
I am thinking of implement a image processing based solution for industrial problem.
The image is consists of a Red rectangle. Inside that I will see a matrix of circles. The requirement is to count the number of circles under following constraints. (Real application : Count the number of bottles in a bottle casing. Any missing bottles???)
The time taken for the operation should be very low.
I need to detect the red rectangle as well. My objective is to count the
items in package and there are no
mechanism (sensors) to trigger the
camera. So camera will need to capture
the photos continuously but the
program should have a way to discard
the unnecessary images.
Processing should be realtime.
There may be a "noise" in image capturing. You may see ovals instead of circles.
My questions are as follows,
What is the best edge detection algorithm that matches with the given
scenario?
Are there any other mechanisms that I can use other than the edge
detection?
Is there a big impact between the language I use and the performance of
the system?
AHH - YOU HAVE NOW TOLD US THE BOTTLES ARE IN FIXED LOCATIONS!
IT IS AN INCREDIBLY EASIER PROBLEM.
All you have to do is look at each of the 12 spots and see if there is a black area there or not. Nothing could be easier.
You do not have to do any edge or shape detection AT ALL.
It's that easy.
You then pointed out that the box might be rotatated, things could be jiggled. That the box might be rotated a little (or even a lot, 0 to 360 each time) is very easily dealt with. The fact that the bottles are in "slots" (even if jiggled) massively changes the nature of the problem. You're main problem (which is easy) is waiting until each new red square (crate) is centered under the camera. I just realised you meant "matrix" literally and specifically in the sentence in your original questions. That changes everything totally, compared to finding a disordered jumble of circles. Finding whether or not a blob is "on" at one of 12 points, is a wildly different problem to "identifying circles in an image". Perhaps you could post an image to wrap up the question.
Finally I believe Kenny below has identified the best solution: blob analysis.
"Count the number of bottles in a bottle casing"...
Do the individual bottles sit in "slots"? ie, there are 4x3 = 12 holes, one for each bottle.
In other words, you "only" have to determine if there is, or is not, a bottle in each of the 12 holes.
Is that correct?
If so, your problem is incredibly easier than the more general problem of a pile of bottles "anywhere".
Quite simply, where do we see the bottles from? The top, sides, bottom, or? Do we always see the tops/bottoms, or are they mixed (ie, packed top-to-tail). These issues make huge, huge differences.
Surf/Sift = overkill in this case you certainly don't need it.
If you want real time speed (about 20fps+ on a 800x600 image) I recommend using Cuda to implement edge detection using a standard filter scheme like sobel, then implement binarization + image closure to make sure the edges of circles are not segmented apart.
The hardest part will be fitting circles. This is assuming you already got to the step where you have taken edges and made sure they are connected using image closure (morphology.) At this point I would proceed as follows:
run blob analysis/connected components to segment out circles that do not touch. If circles can touch the next step will be trickier
for each connected componet/blob fit a circle or rectangle using RANSAC which can run in realtime (as opposed to Hough Transform which I believe is very hard to run in real time.)
Step 2 will be much harder if you can not segment the connected components that form circles seperately, so some additional thought should be invested on how to guarantee that condition.
Good luck.
Edit
Having thought about it some more, I feel like RANSAC is ideal for the case where the circle connected components do touch. RANSAC should hypothetically fit the circle to only a part of the connected component (due to its ability to perform well in the case of mostly outlier points.) This means that you could add an extra check to see if the fitted circle encompasses the entire connected component and if it does not then rerun RANSAC on the portion of the connected component that was left out. Rinse and repeat as many times as necessary.
Also I realize that I say circle but you could just as easily fit an ellipse instead of circles using RANSAC.
Also, I'd like to comment that when I say CUDA is a good choice I mean CUDA is a good choice to implement the sobel filter + binirization + image closing on. Connected components and RANSAC are probably best left to the CPU, but you can try pushing them onto CUDA though I don't know how much of an advantage a GPU will give you for those 2 over a CPU.
For the circles, try the Hough transform.
other mechanisms: dunno
Compiled languages will possibly be faster.
SIFT should have a very good response to circular objects - it is patented, though. GLOHis a similar algorithm, but I do not know if there are any implementations readily available.
Actually, doing some more research, SURF is an improved version of SIFT with quite a few implementations available, check out the links on the wikipedia page.
Sum of colors + convex hull to detect boundary. You need, mostly, 4 corners of a rectangle, and not it's sides?
No motion, no second camera, a little choice - lot of math methods against a little input (color histograms, color distribution matrix). Dunno.
Java == high memory consumption, Lisp == high brain consumption, C++ == memory/cpu/speed/brain use optimum.
If the contrast is good, blob analysis is the algorithm for the job.