A bug in the matrix - the essential matrix that is.
Well, I was really hoping that my next post regarding the my vision project would be to announce that I have phase one working correctly. Alas that is not the case. After some hard debugging over the last couple of weeks I found and fixed a number of bugs and problems.
I am now much more confident in my matching between stereo images, and also in my interframe matching. I rewrote this stuff to be much more in line with the work of Se et al. During the testing process I found a major bug in my kd-tree range search implementation which must have been having a major impact on the number of good quality matches I was getting.
I find it quite difficult to test this stuff as I can't think of a good way of automating the testing of the quality of matching between images. At the moment I simply annotate the images with the SIFT feature locations and then check for matches by eye.
I got really excited about a week ago when I thought I have finally cracked it and was getting reliable interframe matching and ego motion estimation. In fact I still think that for the most part it is working correctly. However, when I checked the 3d world coordinates the system was calculating for the observed features I realised they were completely wrong. Something that was in reality 1 meter away from the center of the camera system was registering as being nearly 3 meters away, and something that was in fact over 2m away came back less than a meter. I think the problem is in the way I am calculating the disparity and I am not taking all the camera intrinsic and extrinsic parameters into account properly.
To that end I am now working on calculating the essential matrix from the fundamental matrix, and then I should be able to much more accurately calculate the relative position of the observed features.
Time will tell.
I am now much more confident in my matching between stereo images, and also in my interframe matching. I rewrote this stuff to be much more in line with the work of Se et al. During the testing process I found a major bug in my kd-tree range search implementation which must have been having a major impact on the number of good quality matches I was getting.
I find it quite difficult to test this stuff as I can't think of a good way of automating the testing of the quality of matching between images. At the moment I simply annotate the images with the SIFT feature locations and then check for matches by eye.
I got really excited about a week ago when I thought I have finally cracked it and was getting reliable interframe matching and ego motion estimation. In fact I still think that for the most part it is working correctly. However, when I checked the 3d world coordinates the system was calculating for the observed features I realised they were completely wrong. Something that was in reality 1 meter away from the center of the camera system was registering as being nearly 3 meters away, and something that was in fact over 2m away came back less than a meter. I think the problem is in the way I am calculating the disparity and I am not taking all the camera intrinsic and extrinsic parameters into account properly.
To that end I am now working on calculating the essential matrix from the fundamental matrix, and then I should be able to much more accurately calculate the relative position of the observed features.
Time will tell.
posted by James Fitzsimons at 5:02 pm
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