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by midjji
738 days ago
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Nonsense. Opencv has been used widely for outdoor localisation too, in particular since it is much easier to do outdoor than indoor. Such algorithms always create a graph over the images, but if you mean the graphslam graph filter methods, those are substantially subpar compared to classic feature based methods as well as the more modern, dense and semidense methods. |
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For localization work (indoor or outdoor), in which you mostly want to close loops and track 3D pose of features using measurements from, say 100s of m or less, (or perhaps scene recognition using effectively flat "very far" images), calibration using opencv is probably highly performant. It's standard, and I've certainly seen much success using it before feeding into regular gtsam etc. There are some unspoken assumptions about localization that don't translate to, say, tracking necessarily. Generally they are: Many observations, close-ish range (relative to stereo offset), mostly-correctly-pointed camera rigs (e.g., forward on a car), or perhaps assumptions about density of features, correlation across images, existence of dense prior maps, etc.
I believe the use case for something like mrcal is to improve calibration for cameras and applications that don't fit this model well. In particular, you may need to track a target at extreme range, using a pixel or two in the corner of your image, with a particularly wide FOV. These specific use cases, mentioned in top level comment, do require additional care, especially in calibration. Thus mrcal.
It just so happens that out of a domain where the very particular details of calibration matter, you may find a tool that helps with all calibration and I think that's the point in bringing up mrcal on a thread discussing calibration in general.
I think that's as precise and non-controversial as I can be.