KumarRobotics / msckf_vio

Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight
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about the covariance divergence #137

Closed boeun98 closed 2 years ago

boeun98 commented 2 years ago

Thanks for your project.

I have two question2.

First,

When I did the VIO, the purple ellipsoid is bigger and bigger.

I think when there aren't the feature points, the purple ellipsoid is bigger and bigger.

It is the position covariance???

I want to know the related equation in the paper.

I want to know why the purple ellipsoid is bigger and bigger.


Second,

I did five laps at the same route.

그림1 . . . . However, the estimated position came out as shown in the picture above.

The height must be constant, but the test results show that it goes up or down.

Can you tell me why?

versatran01 commented 2 years ago

In vio XYZ and yaw are not observable so their covariance will grow. The covariance is simply extracted from the filter's full covariance matrix.

This could be caused by many things, for example, inaccurate intrinsic/extrinsic calibration, wrong gravity norm, etc. It's hard to tell what exactly causes this.

boeun98 commented 2 years ago

Is the z-axis of the path getting smaller because the covariance also increases?

versatran01 commented 2 years ago

I don't understand your question

boeun98 commented 2 years ago

I'm talking about the figure above.

I experimented on the track (first figure).

But, the trajectory result is the second figure.

Is this result also due to increased covariance?

and Can you tell me the equation about covariance that you said? . . image01 . . . front_3차원_2

versatran01 commented 2 years ago

Could be, but most likely it's the other way around.

See details at https://en.wikipedia.org/wiki/Kalman_filter#Details