I want to locate a service robot via infrared landmarks. The idea is to detect two landmarks, get the distance to the landmarks and calculate the robots position from these informations (the position of the landmarks are known).
For this I have built an artificial 2x3 matrix of IR LEDs, which are visible in the robots infrared camera image (shown in the image below).
As the first step, I want to detect a single landmark in a picture and get it's x-y coordinates. I can use these coordinates in the future to get the distance from the depth-image provided.
My first approach was to convert the image to a black and white image. Then I tried to filter out different cluster of points (which i dilated and contoured in the first place). I couldn't succeed with this method.
Now I wonder if there are any pattern recognition/computer vision methods which can help me to quite "easily" detect the pattern.
I've added a picture of the infrared image with the landmark in it and a converted black/white image.
a) Which method can help me to solve this problem?
b) Should I use a 3x3 Matrix or any other geometric form instead of the 2x3 Matrix ?
A direct answer: 1) find all small circles in the image; 2) look among these small circles for ones that are the same size and close together, and, say, form parallel lines.
The reason for this approach is that you have coded the robot with a specific pattern of small objects. Therefore, look for the objects and then look for the pattern. (If the orientation and size wouldn't change, then you could just look for a sub-image within the larger image, but because it can, you need to look for elements of the pattern that remain consistent with motion in the 3D space, that is, the parallel lines.)
This will work in the example images, but to know whether this will work more generally, we need to know more than you told us: It depends on whether the variation in the images of the matrix and the variations in the background will let this be enough to distinguish between them. If not, maybe you need a more clever algorithm or maybe a different pattern of lights. In the extreme case, it's obvious that if you had another 2x3 matric around, it's not enough. It all depends on the variation of the object to be identified and the variations within the background scene, and because you don't tell us either of these things, it's hard to say the best way, what's good enough, what's a better way, etc.
If you have the choice, and here it sound like you do, good data is better than clever analysis. For this problem, I'd call good data to be anything that clearly distinguishes the object from the background. You need to think of it this way, and look at what the background is, and all the different perspectives on the lights that are possible, and make sure these can never be confused.
For example, if you have a lot of control over this, and enough time, temporal variations are often the easiest. Turning the lights (or a subset of the lights) on and off, etc, and then looking for the expected temporal variation is often the surest way to distinguish signal from noise — but really, this again is just making an assumption about the background and foreground (ie, that the background won't vary with some particular time pattern).
