ledstripe: Spin faster than 1s

[ros_wild_thumper.git] / config / robot_localization.yaml

diff --git a/config/robot_localization.yaml b/config/robot_localization.yaml
index ca599f95fa899053a387a75e628db6bca4442151..82d5fff34d9f547cfc3d0a57e069803e1242ec56 100644 (file)
--- a/config/robot_localization.yaml
+++ b/config/robot_localization.yaml
@@ -46,13 +46,14 @@ world_frame: odom
 # Use this parameter to provide an offset to the transform generated by ekf_localization_node. This
 # can be used for future dating the transform, which is required for interaction with some other
 # packages. Defaults to 0.0 if unspecified.
-#transform_time_offset: 0.0
+#transform_time_offset: 0.05
 
 # The filter accepts an arbitrary number of inputs from each input message type (Odometry, PoseStamped,
 # TwistStamped, Imu). To add a new one, simply append the next number in the sequence to its base name,
 # e.g., odom0, odom1, twist0, twist1, imu0, imu1, imu2, etc. The value should be the topic name. These
 # parameters obviously have no default values, and must be specified.
 odom0: odom
+odom1: gps
 imu0: imu
 imu0_queue_size: 10
 
@@ -63,13 +64,20 @@ 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,
-               false, false, false, 
+               false, false, false,
                true,  true,  true,
                false, false, true,
                false, false, false]
+# gps
+odom1_config: [true,  true,  false,
+               false, false, false,
+               false, false, false,
+               false, false, false,
+               false, false, false]
 imu0_config:  [false, false, false,
                true,  true,   true,
                false, false, false,
@@ -84,15 +92,16 @@ imu0_config:  [false, false, false,
 #  either increase the covariances for the variables in question, or you can simply set the sensor's differential
 #  parameter to true. When differential mode is enabled, all absolute pose data is converted to velocity data by
 #  differentiating the absolute pose measurements. These velocities are then integrated as usual. NOTE: this only
-#  applies to sensors that provide absolute measurements, so setting differential to true for twit measurements has
+#  applies to sensors that provide absolute measurements, so setting differential to true for twist measurements has
 #  no effect.
-#  
+#
 #  Users should take care when setting this to true for orientation variables: if you have only one source of
 #  absolute orientation data, you should not set the differential parameter to true. This is due to the fact that
 #  integration of velocities leads to slowly increasing error in the absolute (pose) variable. For position variables,
 #  this is acceptable. For orientation variables, it can lead to trouble. Users should make sure that all orientation
 #  variables have at least one source of absolute measurement.
 odom0_differential: false
+odom1_differential: false
 imu0_differential: false
 
 #  When the node starts, if this parameter is true, then the first measurement is treated as a "zero point" for all
@@ -100,6 +109,7 @@ imu0_differential: false
 #  that the relative parameter doesn't cause the measurement to be converted to a velocity before integrating it. If
 #  you simply want your measurements to start at 0 for a given sensor, set this to true.
 odom0_relative: false
+odom1_relative: false
 imu0_relative: false
 
 # If we're including accelerations in our state estimate, then we'll probably want to remove any acceleration that
@@ -113,3 +123,26 @@ 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
 # robots estimate error covariance to stop growing when the robot is stationary. Defaults to false.
 dynamic_process_noise_covariance: true
+
+# The process noise covariance matrix can be difficult to tune, and can vary for each application, so it is
+# exposed as a configuration parameter. This matrix represents the noise we add to the total error after each
+# prediction step. The better the omnidirectional motion model matches your system, the smaller these values can be.
+# However, if users find that a given variable is slow to converge, one approach is to increase the
+# 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: [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,
+                           0,    0,    0,    0,    0,    0.06, 0,     0,     0,    0,    0,    0,    0,    0,    0,
+                           0,    0,    0,    0,    0,    0,    0.025, 0,     0,    0,    0,    0,    0,    0,    0,
+                           0,    0,    0,    0,    0,    0,    0,     0.025, 0,    0,    0,    0,    0,    0,    0,
+                           0,    0,    0,    0,    0,    0,    0,     0,     0.04, 0,    0,    0,    0,    0,    0,
+                           0,    0,    0,    0,    0,    0,    0,     0,     0,    0.01, 0,    0,    0,    0,    0,
+                           0,    0,    0,    0,    0,    0,    0,     0,     0,    0,    0.01, 0,    0,    0,    0,
+                           0,    0,    0,    0,    0,    0,    0,     0,     0,    0,    0,    0.02, 0,    0,    0,
+                           0,    0,    0,    0,    0,    0,    0,     0,     0,    0,    0,    0,    0.01, 0,    0,
+                           0,    0,    0,    0,    0,    0,    0,     0,     0,    0,    0,    0,    0,    0.01, 0,
+                           0,    0,    0,    0,    0,    0,    0,     0,     0,    0,    0,    0,    0,    0,    0.015]