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Probably because this is just 2D tracking rather than 3D mapping. But tracking can be applied to mapping, for example: http://www.robots.ox.ac.uk/~gk/PTAM/

So the question is, can predator be used to improve mapping? AFAIK, that would require a) automatically selecting trackable objects and b) tracking many of them simultaneously. That PTAM technique tracks thousands, but with tracking this reliable, you might get by with much less.

So, more work is required to apply it to mapping, but I have to imagine it could be done. And seeing how well predator adapts to changes in scale, orientation, and visibility, I suspect it could improve mapping considerably.




"AFAIK, that would require a) automatically selecting trackable objects and b) tracking many of them simultaneously."

I'm not really sure I understood you, but this two problems are already solved. Hugin[1] for example has automatic control point generation for photo stitching. Were you talking about something else?

[1]http://hugin.sourceforge.net/releases/2010.4.0/en.shtml

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Yeah, there are many ways to detect features but I haven't read the paper yet and I don't know what kind of features it wants and if there are any problems with choosing them automatically. Like, can it group features into distinct objects without a human pointing them out?

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