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Any ideas/thoughts on how can we estimate the motor odometry covariance values? I am planning to use motor odometry output from a holonomic robot and feed it into the robot_localization ekf filter. I am not sure how important are the measurement noise in the filter and how they should be estimated.
There's plenty of research on the topic. Just a quick Google search yields a lot:
But as a naive (and possibly not completely mathematically valid) method, you can use some ground truth. Drive your robot a known distance (measured with tape on the floor). Look at the difference in what your wheel encoders estimate as the distance traveled and what it actually travelled. Count the number of velocity measurements that were generated in between, and then you have a rough estimate for the error per measurement. Square that value and make it your linear velocity covariance. You may want to repeat this for each axis, as your robot is holonomic.
The good news is that, if you don't have the time to do it properly, Bayes filters tend to behave well even when the error is over-estimated.
Asked: 2020-03-12 17:24:28 -0500
Seen: 1,296 times
Last updated: Apr 27 '20
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could you add please "covariance" as a tag or in the title, so it will be more easily picked at search?