98 lines
2.4 KiB
Plaintext
98 lines
2.4 KiB
Plaintext
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stereo callback()
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create image pyramids
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_Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK._
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.
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if first Frame:
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*initialize first Frame ()
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else:
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*track Features ()
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*addnewFeatures ()
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*pruneGridFeatures()
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_removes worst features from any overflowing grid_
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publish features (u1, v1, u2, v2)
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_undistorts them beforehand_
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addnewFeatures()
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*mask existing features
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*detect new fast features
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*collect in a grid, keep only best n per grid
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*stereomatch()
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*save inliers into a new feature with u,v on cam0 and cam1
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track Features()
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*integrateIMUData ()
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_uses existing IMU data between two frames to calc. rotation between the two frames_
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*predictFeatureTracking()
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_compensates the rotation between consecutive frames - rotates previous camera frame features to current camera rotation_
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*calcOpticalFlowPyrLK()
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_measures the change between the features in the previous frame and in the current frame (using the predicted features)_
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*remove points outside of image region
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_how does this even happen?_
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*stereo match()
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_find tracked features from optical flow in the camera 1 image_
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_remove all features that could not be matched_
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*twoPointRansac(cam0)
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*twoPointRansac(cam1)
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_remove any features outside best found ransac model_
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twoPointRansac()
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*mark all points as inliers
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*compensate rotation between frames
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*normalize points
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*calculate difference bewteen previous and current points
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*mark large distances (over 50 pixels currently)
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*calculate mean points distance
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*return if inliers (non marked) < 3
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*return if motion smaller than norm pixel unit
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*ransac
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*optimize with found inlier after random sample
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*set inlier markers
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initialize first Frame()
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features = FastFeatureDetector detect ()
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*stereo match ()
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group features into grid
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- according to position in the image
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- sorting them by response
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- save the top responses
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- save the top responses
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stereo match ()
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*undistort cam0 Points
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*project cam0 Points to cam1 to initialize points in cam1
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*calculate lk optical flow
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_used because camera calibrations not perfect enough_
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_also, calculation more efficient, because LK calculated anyway_
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*compute relative trans/rot between cam0 and cam1*
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*remove outliers based on essential matrix
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_essential matrix relates points in stereo image (pinhole model)_
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for every point:
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- calculate epipolar line of point in cam0
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- calculate error of cam1 to epipolar line
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- remove if to big
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