# message types lack certain variables. For example, a TwistWithCovarianceStamped message has no pose information, so
# the first six values would be meaningless in that case. Each vector defaults to all false if unspecified, effectively
# making this parameter required for each sensor.
+# odom
# x/y not included because of redundancy with velocities
# vyaw not included in odom because too inaccurate
odom0_config: [false, false, false,
true, true, true,
false, false, true,
false, false, false]
+# gps
odom1_config: [true, true, false,
false, false, false,
false, false, false,
# if the node is unhappy with any settings or data.
print_diagnostics: true
-# If true, will dynamically scale the process_noise_covariance based on the robot?s velocity. This is useful, e.g., when you want your
+# If true, will dynamically scale the process_noise_covariance based on the robot's velocity. This is useful, e.g., when you want your
# robots estimate error covariance to stop growing when the robot is stationary. Defaults to false.
dynamic_process_noise_covariance: true
# process_noise_covariance diagonal value for the variable in question, which will cause the filter's predicted error
# to be larger, which will cause the filter to trust the incoming measurement more during correction. The values are
# ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az.
-process_noise_covariance: [0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+process_noise_covariance: [2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.06, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,