83 lines
2.6 KiB
Plaintext
83 lines
2.6 KiB
Plaintext
featureCallback
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propagate IMU state()
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state Augmentation()
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add Feature Observations()
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#the following possibly trigger ekf update step:
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remove Lost Features ()
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prune Camera State Buffer ()
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remove Lost Features()
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every feature that does not have a current observation:
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*just delete if not enough features
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check Motion of Feature ()
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_calculation here makes no sense - he uses pixel position as direction vector for feature?_
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*initialize Position ()
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caculate feature Jakobian and Residual()
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*for every observation in this feature
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- calculate u and v in camera frames, based on estimated feature position
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- input results into jakobi d(measurement)/d(camera 0/1)
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- input results into jakobi d(camera 0/1)/d(state) and jakobi d(camera 0/1)/d(feature position)
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- project both jakobis to nullspace of feature position jakobi
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- calculate residual: measurement - u and v of camera frames
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- project residual onto nullspace of feature position jakobi
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- stack residual and jakobians
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gating Test()
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*measurementUpdate()
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_use calculated residuals and jakobians to calculate change in error_
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measurementUpdate():
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- QR reduce the stacked Measurment Jakobis
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- calcualte Kalman Gain based on Measurement Jakobian, Error-State Covariance and Noise
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_does some fancy shit here_
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- calculate estimated error after observation: delta_x = KalmanGain * residual
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- add estimated error to state (imu states and cam states)
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initialize Position ():
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* create initial guess for global feature position ()
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_uses first feature measurement on left camera and last feature measurement of right camera_
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- transform first measurement to plane of last measurement
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- calcualte least square point between rays
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* get position approximation using measured feature positions
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_using Levenberg Marqhart iterative search_
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add Feature Observations()
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* if feature not in map, add feature to map
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_and add u0, v0, u1, v1 as first observation
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* if feature in map, add new observation u0,v0,u1,v1
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state Augmentation()
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* Add estimated cam position to state
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* Update P with Jakobian of cam Position
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propagate IMU state ()
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_uses IMU process model for every saved IMU state_
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for every buffered imu state:
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*remove bias
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*Compute F and G matrix (continuous transition and noise cov.)
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_using current orientation, gyro and acc. reading_
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* approximate phi: phi = 1 + Fdt + ...
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* calculate new state propagating through runge kutta
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* modify transition matrix to have a propper null space?
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* calculate Q = Phi*G*Q_noise*GT*PhiT
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* calculate P = Phi*P*PhiT + Q
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stateAugmentation ()
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_save current IMU state as camera position_
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