6 Mar 2003 motters   » (Master)

In order to get better occupancy grid results I'm trying to improve the accuracy of the stereo matching algorithm.

Pretty much all the stereo algorithms which other people have devised perform abominably, even on high quality images. There are some examples of stereo matching from various researchers which at first glance appear to give impressive results, but when I substitute in my own images these algorithms produce an unintelligable fog. Traditionally stereo matching algorithms look for unique feature points in both images and then attempt to match them. The only system to date which performs half decently is one devised by Stan Birchfield, and my own system is based on his idea.

Instead of trying to look for identifiable feature points I decompose the image into a set of horizontal slices, where eash slice represents an area of continuous colour or texture. I then attempt to match the slices between the two images. Matching of the slices occurs using the weighted sum of various attributes, such as colour, length and position relative to neerby slices.

Until recently my stereo matching system only used mono images. The original colour images were converted to mono and then matched. However, I found that using colour gives a huge improvement in matching accuracy.

The algorithm that I've got at the moment is pretty good, but there is still room for improvement. Matching of horizontal slices is now about 90% accurate, but the estimation of distance based upon horizontal displacement still seems fairly inaccurate.

I'm developing the new stereo algorithm in isolation at the moment using VB and taking images from the robot in order to test the accuracy of the depth images. The system runs sluggishly in VB, but once perfected I'll tansfer it back to C++.

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