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And also fixed F computation for features.
I realized that the features were transformed from image to body frame
prior to being passed to State. This makes sense because the State
really doesn't need to know about Camera objects. Since the camera was
no longer necessary inside of State it made sense to move the depth
computation from Camera::ref2body() into a method in the Feature class
that does not rely on camera parameters.
Additionally, the depth solver was changed from a simple matrix
inversion to a least squares calculation.
Fx in the feature class was fixed to take into account dt.
Updating Pkk1 is in the process of being modified to handle features.
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This fixes a bug that was present in the pyslam code. The Jacobian of the motion
model needs to be replaced with:
F = I + F*dt
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This is a method for computing S for an independent measurement. It can
provide a speed improvement over computing a full, correlated S, but may
reduce quality if correlated measurements are treated as independent.
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The motion model is broken into two parts, the creation of the L matrix
and the state update. This means that ydot can be computed from the
state object for many features at once, saving computation time.
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Used a slightly simplified expression to compute pib0. Replaced the
Householder Transform with quaternion based reflection (untested).
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