featureCallback
	propagate IMU state()
	state Augmentation()
	add Feature Observations()

	#the following possibly trigger ekf update step:
	remove Lost Features ()
	prune Camera State Buffer ()


remove Lost Features()
	every feature that does not have a current observation:
		*just delete if not enough features

		check Motion of Feature ()
			_calculation here makes no sense - he uses pixel position as direction vector for feature?_
		*initialize Position ()

		caculate feature Jakobian and Residual()
			*for every observation in this feature
				- calculate u and v in camera frames, based on estimated feature position
				- input results into jakobi d(measurement)/d(camera 0/1)
				- input results into jakobi d(camera 0/1)/d(state) and jakobi d(camera 0/1)/d(feature position)
				- project both jakobis to nullspace of feature position jakobi
				- calculate residual: measurement - u and v of camera frames
				- project residual onto nullspace of feature position jakobi

			- stack residual and jakobians

		gating Test()
		
		*measurementUpdate()
			_use calculated residuals and jakobians to calculate change in error_
measurementUpdate():
	- QR reduce the stacked Measurment Jakobis
	- calcualte Kalman Gain based on Measurement Jakobian, Error-State Covariance and Noise
		_does some fancy shit here_
	- calculate estimated error after observation: delta_x = KalmanGain * residual 
	- add estimated error to state (imu states and cam states)

initialize Position ():
	* create initial guess for global feature position ()
		_uses first feature measurement on left camera and last feature measurement of right camera_
		- transform first measurement to plane of last measurement
		- calcualte least square point between rays
	* get position approximation using measured feature positions
		_using Levenberg Marqhart iterative search_



add Feature Observations()
	* if feature not in map, add feature to map
		_and add u0, v0, u1, v1 as first observation
	* if feature in map, add new observation u0,v0,u1,v1



state Augmentation()
	* Add estimated cam position to state
	* Update P with Jakobian of cam Position 


propagate IMU state ()
	_uses IMU process model for every saved IMU state_
	
	for every buffered imu state:
		
		*remove bias
		
		*Compute F and G matrix (continuous transition and noise cov.)
			_using current orientation, gyro and acc. reading_
		* approximate phi: phi = 1 + Fdt + ...

		* calculate new state propagating through runge kutta
		* modify transition matrix to have a propper null space?

		* calculate Q = Phi*G*Q_noise*GT*PhiT
		* calculate P = Phi*P*PhiT + Q


	stateAugmentation ()
		_save current IMU state as camera position_