global_costmap:

[ros_wild_thumper.git] / config / robot_localization.yaml

diff --git a/config/robot_localization.yaml b/config/robot_localization.yaml
index 630e24d2822f96dc2f687b31710232a1722c4026..bce9d7466b012d5ed373181739ef0f08ae2dc5a7 100644 (file)
--- a/config/robot_localization.yaml
+++ b/config/robot_localization.yaml
@@ -64,6 +64,7 @@ imu0_queue_size: 10
 #  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,
@@ -71,6 +72,7 @@ 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,
@@ -118,7 +120,7 @@ imu0_remove_gravitational_acceleration: true
 # 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
 
@@ -129,8 +131,8 @@ 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,