IR pose estimation #4
Open
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This is my first spike at non-GT state estimation. There is a flag
self.imu_enabledinIREstimator, which lets you toggle between IMU and prediction-based state evolution in the absence of IR markers. It doesn't really matter which one you use, both are by far too inaccurate to be used by themselves.We really need an unbiased velocity estimator like visual odometry, otherwise position drift will accumulate too rapidly due to cascading integral error, as now velocity is integrated from acceleration.
Combining IMU and prediction using an EKF would surely bring the error down, but I think the fundamental problem is that the velocity estimate is biased, which will always lead to super-linear position error growth with respect to time.