EKF2: Fix Joseph covariance update for direct state observations #22911
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This makes the ekf unstable and creates NANs during initialization
The duration of a unit test had to be increased because the incorrect covariance matrix update, was making the unit test passing faster (over-optimistic variance).
bresch
commented
Mar 20, 2024
Comment on lines
+884
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| // P (or "P_temp") is not symmetric so we must take the column | ||
| PH = P.col(state_index); |
|
CI checks failing:
I'll merge this important fix anyway. |
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Solved Problem
I found that the EKF would crash if gyro bias estimation is disabled.
Solution
Same issue as in #22847 , the intermediate covariance matrix isn't symmetric, so taking the row and the column is different. We must take the column to get correct results when the Kalman gain is manually modified (inhibited states).
I added more comments to explain how to the original Joseph covariance equation is manipulated to obtain an efficient implementation.
Changelog Entry
For release notes:
Test coverage
added unit test to reproduce the bug