How to fit more than one line to data points

Question

I am trying to fit more than one line to a list of points in 2D. My points are quite low in number (16 or 32).

These points are coming from a simulated environment of a robot with laser range finders attached to its side. If the points lie on a line it means that they detected a wall, if not, it means they detected an obstacle. I am trying to detect the walls and calculate their intersection, and for this I thought the best idea is to fit lines on the dataset.

Fitting one line to a set of points is not a problem, if we know all those points line on or around a line.

My problem is that I don't know how can I detect which sets of points should be classified for fitting on the same line and which should not be, for each line. Also, I don't even now the number of points on a line, while naturally it would be the best to detect the longest possible line segment.

How would you solve this problem? If I look at all the possibilities for example for groups of 5 points for all the 32 points then it gives 32 choose 5 = 201376 possibilities. I think it takes way too much time to try all the possibilities and try to fit a line to all 5-tuples.

So what would be a better algorithm what would run much faster? I could connect points within limit and create polylines. But even connecting the points is a hard task, as the edge distances change even within a single line.

Do you think it is possible to do some kind of Hough transform on a discrete dataset with such a low number of entries?

Note: if this problem is too hard to solve, I was thinking about using the order of the sensors and use it for filtering. This way the algorithm could be easier but if there is a small obstacle in front of a wall, it would distract the continuity of the line and thus break the wall into two halves.

Answer 1

A good way to find lines in noisy point data like this is to use RANSAC. Standard RANSAC usage is to pick the best hypothesis (=line in this case) but you can just as easy pick the best 2 or 4 lines given your data. Have a look at the example here: http://www.janeriksolem.net/2009/06/ransac-using-python.html Python code is available here http://www.scipy.org/Cookbook/RANSAC

Answer 2

The first thing I would point out is that you seem to be ignoring a vital aspect of the data, which is you know which sensors (or readings) are adjacent to each other. If you have N laser sensors you know where they are bolted to the robot and if you are rotating a sensor you know the order (and position) in which the measurements are taken. So, connecting points together to form a piecewise linear fit (polylines) should be trivial. Once you had these correspondances you could take each set of four points and determine if they can be modeled effectively by only 2 lines, or something.

Secondly, it's well known that finding a globally optimal fit for even two lines to an arbitrary set of points is NP-Hard as it can be reduced to k-means clustering, so I would not expect to find an efficient algorithm for this. When I used the Hough transform it was for finding corners based on pixel intensities, in theory it is probably applicable to this sort of problem but it is just an approximation and it is probably going to take a fair bit of work to figure out and implement.

I hate to suggest it but it seems like you might benefit by looking at the problem in a slightly different way. When I was working in autonomous navigation with a laser range finder we solved this problem by discretizing the space into an occupancy grid, which is the default approach. Of course, this just assumes the walls are just another object, which is not a particularly outrageous idea IMO. Beyond that, can you assume you have a map of the walls and you are just trying to find the obstacles and localize (find the pose of) the robot? If so, there are a large number of papers and tech reports on this problem.