Revision history [back]

For any given measurement, there are two covariance matrices at play:

1. The state's covariance matrix
2. The measurement's covariance matrix

The one that concerns me in your case is your state covariance. You can set the initial value for this matrix using the initial_estimate_covariance parameter. Here's the rule you should follow: if you are measuring a variable, make the diagonal value in initial_estimate_covariance larger than that measurement's covariance. So, for example, if your measurement's covariace value for the variable in question is 1e-6, make the initial_estimate_covariance diagonal value 1e-3 or something. This will speed up convergence. If you are not measuring a given variable (e.g., roll), make the diagonal value in initial_estimate_covariance small (but never zero).

For any given measurement, there are two covariance matrices at play:

1. The state's covariance matrix
2. The measurement's covariance matrix

The one that concerns me in your case is your state covariance. You can set the initial value for this matrix using the initial_estimate_covariance parameter. Here's the rule you should follow: if you are measuring a variable, make the diagonal value in initial_estimate_covariance larger than that measurement's covariance. So, for example, if your measurement's covariace value for the variable in question is 1e-6, make the initial_estimate_covariance diagonal value 1e-3 or something. This will speed up convergence. If you are not measuring a given variable (e.g., roll), make the diagonal value in initial_estimate_covariance small (but never zero).

For any given measurement, there are two covariance matrices at play:

1. The state's covariance matrix
2. The measurement's covariance matrix

The one that concerns me in your case is your state covariance. You can set the initial value for this matrix using the initial_estimate_covariance parameter. Here's the rule you should follow: if you are measuring a variable, make the diagonal value in initial_estimate_covariance larger than that measurement's covariance. So, for example, if your measurement's covariace value for the variable in question is 1e-6, make the initial_estimate_covariance diagonal value 1e-3 or something. This will speed up convergence. If you are not measuring a given variable (e.g., roll), make the diagonal value in initial_estimate_covariance small (but never zero).

EDIT 1

It's hard to tell with the time scale what the lag is between the amcl yaw and the robot_localization yaw. For your plots, you might try (a) zooming in a bit more, and (b) subtracting the first time stamp from one of the data sources from all the timestamps from all sources so that we can see the time in the X axis better.

Also, can you turn OFF the yaw velocity from the wheel encoders in your second r_l config? I want to see the delay when you only have one sensor input.

Also, try increasing the process_noise_covariance for yaw. It will make the error in the state estimate grow faster, which will in turn cause it to trust your measurements more.

Also also, you might want to try the transform_time_offset parameter. I believe amcl future-dates its map->odom transform (with a default value of 0.1 seconds), so it may be worth doing the same.