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The simplest explanation will be: In structure from motion, it estimates structure(xyz points), camera locations, camera intrinsic. In graph optimization, it only estimates camera locations. In the graph SLAM, the structure is just a by-product of a corrected trajectory or graph nodes. E.g. implementing Bundle adjustment with g2o -> You can do it by ...


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The answer simply is, it does not really matter because you're using the norm. The scale is determined by the actual translation and rotation between two cameras (which in case of monocular odometry are two views from the same calibrated camera). This rotation and translation information is contained in the essential matrix (or fundamental matrix) which has ...


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It turns out this can easily be done with OpenCV - just find image features (FAST etc.) in first image, track them to the second image (get a set of corresponding features between two images) and then use triangulatePoints function to get the 3D scene. triangulatePoints accepts two projection matrices - one for each image. Each projection matrix defines ...


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