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SVO algorithm fails to track more than 80 frames on the ICL-NUIM Living Room dataset #87
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Apart from the texture I think there might be a problem that the focal length in this dataset is negative. i remember that i had some issues with that when i wanted to use this dataset to test sparse image alignment. |
The "fy" of the camera is negative, indeed. |
I tested with the re-textured version (applied GIMP Retinex filter on every texture) of "lr kt3". At least one of the problems has been solved: However, after around frame 130, SVO mentions a drop of features larger than 50, and as a result the relocalizer kicks in.
You might say the last parameter-setting is satisfactory enough, |
@Eliasvan can you check if you get better results when you put a minus before 'dy' on line 140 in sparse_img_align.cop? |
Thank you very much for your reply, you hit the nail on the head: Here is the AbsoluteTrajectoryError (http://www.tinyupload.org/c6b2ve5k9kw) and here is the RelativePoseError (http://www.tinyupload.org/o62c8nanpdf). I also tested on the "lr_kt2" trajectory, and this one required some minor tuning of the config: To summarize, after applying your fix, the re-texturing was absolutely necessary to make SVO work on the ICL NUIM living_room dataset. There is one last thing I'm not yet entirely satisfied about yet: the output map seems reasonable, but it seems that outliers are not filtered (as can be seen in the linked video). Will you apply a fix for this issue? Once again, thanks for your response! |
Hi, Just to let you know, I've made a video where our simple SLAM system is run on the re-textured dataset. Elias |
Hi Elias, |
Although there is texture in the "lr kt3" sequence (http://www.doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html), SVO fails to track the whole (1240 frames) sequence, it only tracks ~80 frames.
I made a video of the results (compared with our simple SLAM system):
https://www.youtube.com/watch?v=khSYi-s7mM4 (relevant part starts at 5:17)
However, I'm aware that SVO works best with wide FOV cams and sidewards motion, which this sequence doesn't satisfy well, but I also tested on "lr kt2" (more sidewards motion) and it doesn't work properly either.
I'll retry with more texture, maybe that will solve the problem.
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