diff --git a/launch/rosbot1_ekf.launch b/launch/rosbot1_ekf.launch
new file mode 100644
index 0000000..eace1d9
--- /dev/null
+++ b/launch/rosbot1_ekf.launch
@@ -0,0 +1,10 @@
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/launch/rosbot1_hardware.launch b/launch/rosbot1_hardware.launch
index 453e1b2..e635f04 100644
--- a/launch/rosbot1_hardware.launch
+++ b/launch/rosbot1_hardware.launch
@@ -25,7 +25,7 @@
-
+
diff --git a/launch/rosbot2_ekf.launch b/launch/rosbot2_ekf.launch
new file mode 100644
index 0000000..69313f9
--- /dev/null
+++ b/launch/rosbot2_ekf.launch
@@ -0,0 +1,10 @@
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/launch/rosbot2_hardware.launch b/launch/rosbot2_hardware.launch
index 10b115d..54b0c7e 100644
--- a/launch/rosbot2_hardware.launch
+++ b/launch/rosbot2_hardware.launch
@@ -25,7 +25,7 @@
-
+
diff --git a/params/ekf1_params.yaml b/params/ekf1_params.yaml
new file mode 100644
index 0000000..5f3ddf1
--- /dev/null
+++ b/params/ekf1_params.yaml
@@ -0,0 +1,206 @@
+frequency: 20
+
+sensor_timeout: 0.2
+
+two_d_mode: true
+
+# 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
+
+# Use this parameter to provide specify how long the tf listener should wait for a transform to become available.
+# Defaults to 0.0 if unspecified.
+transform_timeout: 0.0
+
+# If you're having trouble, try setting this to true, and then echo the /diagnostics_agg topic to see if the node is
+# unhappy with any settings or data.
+print_diagnostics: true
+
+debug: false
+
+# Defaults to "robot_localization_debug.txt" if unspecified. Please specify the full path.
+debug_out_file: /path/to/debug/file.txt
+
+# Whether to broadcast the transformation over the /tf topic. Defaults to true if unspecified.
+publish_tf: true
+
+# Whether to publish the acceleration state. Defaults to false if unspecified.
+publish_acceleration: false
+
+# REP-105 (http://www.ros.org/reps/rep-0105.html) specifies four principal coordinate frames: base_link, odom, map, and
+# earth. base_link is the coordinate frame that is affixed to the robot. Both odom and map are world-fixed frames.
+# The robot's position in the odom frame will drift over time, but is accurate in the short term and should be
+# continuous. The odom frame is therefore the best frame for executing local motion plans. The map frame, like the odom
+# frame, is a world-fixed coordinate frame, and while it contains the most globally accurate position estimate for your
+# robot, it is subject to discrete jumps, e.g., due to the fusion of GPS data or a correction from a map-based
+# localization node. The earth frame is used to relate multiple map frames by giving them a common reference frame.
+# ekf_localization_node and ukf_localization_node are not concerned with the earth frame.
+# Here is how to use the following settings:
+# 1. Set the map_frame, odom_frame, and base_link frames to the appropriate frame names for your system.
+# 1a. If your system does not have a map_frame, just remove it, and make sure "world_frame" is set to the value of
+# odom_frame.
+# 2. If you are fusing continuous position data such as wheel encoder odometry, visual odometry, or IMU data, set
+# "world_frame" to your odom_frame value. This is the default behavior for robot_localization's state estimation nodes.
+# 3. If you are fusing global absolute position data that is subject to discrete jumps (e.g., GPS or position updates
+# from landmark observations) then:
+# 3a. Set your "world_frame" to your map_frame value
+# 3b. MAKE SURE something else is generating the odom->base_link transform. Note that this can even be another state
+# estimation node from robot_localization! However, that instance should *not* fuse the global data.
+map_frame: /map # Defaults to "map" if unspecified
+odom_frame: rosbot1/odom # Defaults to "odom" if unspecified
+base_link_frame: rosbot1/base_link # Defaults to "base_link" if unspecified
+world_frame: rosbot1/odom # Defaults to the value of odom_frame if unspecified
+
+# The filter accepts an arbitrary number of inputs from each input message type (nav_msgs/Odometry,
+# geometry_msgs/PoseWithCovarianceStamped, geometry_msgs/TwistWithCovarianceStamped,
+# sensor_msgs/Imu). To add an input, 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: rosbot1/odom/wheel
+
+# Each sensor reading updates some or all of the filter's state. These options give you greater control over which
+# values from each measurement are fed to the filter. For example, if you have an odometry message as input, but only
+# want to use its Z position value, then set the entire vector to false, except for the third entry. The order of the
+# values is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Note that not some message types
+# do not provide some of the state variables estimated by the filter. 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.
+odom0_config: [true, true, true,
+ true, true, true,
+ false, false, false,
+ false, false, false,
+ false, false, false]
+
+# If you have high-frequency data or are running with a low frequency parameter value, then you may want to increase
+# the size of the subscription queue so that more measurements are fused.
+odom0_queue_size: 6
+
+# [ADVANCED] Large messages in ROS can exhibit strange behavior when they arrive at a high frequency. This is a result
+# of Nagle's algorithm. This option tells the ROS subscriber to use the tcpNoDelay option, which disables Nagle's
+# algorithm.
+odom0_nodelay: false
+
+# [ADVANCED] When measuring one pose variable with two sensors, a situation can arise in which both sensors under-
+# report their covariances. This can lead to the filter rapidly jumping back and forth between each measurement as they
+# arrive. In these cases, it often makes sense to (a) correct the measurement covariances, or (b) if velocity is also
+# measured by one of the sensors, let one sensor measure pose, and the other velocity. However, doing (a) or (b) isn't
+# always feasible, and so we expose the differential parameter. 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 pose measurements; setting differential to true
+# for twist measurements has no effect.
+odom0_differential: false
+
+# [ADVANCED] When the node starts, if this parameter is true, then the first measurement is treated as a "zero point"
+# for all future measurements. While you can achieve the same effect with the differential paremeter, the key
+# difference is 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: true
+
+# [ADVANCED] If your data is subject to outliers, use these threshold settings, expressed as Mahalanobis distances, to
+# control how far away from the current vehicle state a sensor measurement is permitted to be. Each defaults to
+# numeric_limits::max() if unspecified. It is strongly recommended that these parameters be removed if not
+# required. Data is specified at the level of pose and twist variables, rather than for each variable in isolation.
+# For messages that have both pose and twist data, the parameter specifies to which part of the message we are applying
+# the thresholds.
+#odom0_pose_rejection_threshold: 5
+#odom0_twist_rejection_threshold: 1
+
+# values is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az
+
+#-----------------------------------------
+
+imu0: rosbot1/imu
+imu0_config: [false, false, false,
+ false, false, true,
+ false, false, false,
+ false, false, true,
+ false, false, false]
+imu0_nodelay: false
+imu0_differential: true
+imu0_relative: true
+imu0_queue_size: 4
+imu0_pose_rejection_threshold: 0.8 # Note the difference in parameter names
+imu0_twist_rejection_threshold: 0.8 #
+imu0_linear_acceleration_rejection_threshold: 0.8 #
+
+# [ADVANCED] Some IMUs automatically remove acceleration due to gravity, and others don't. If yours doesn't, please set
+# this to true, and *make sure* your data conforms to REP-103, specifically, that the data is in ENU frame.
+imu0_remove_gravitational_acceleration: true
+
+# [ADVANCED] The EKF and UKF models follow a standard predict/correct cycle. During prediction, if there is no
+# acceleration reference, the velocity at time t+1 is simply predicted to be the same as the velocity at time t. During
+# correction, this predicted value is fused with the measured value to produce the new velocity estimate. This can be
+# problematic, as the final velocity will effectively be a weighted average of the old velocity and the new one. When
+# this velocity is the integrated into a new pose, the result can be sluggish covergence. This effect is especially
+# noticeable with LIDAR data during rotations. To get around it, users can try inflating the process_noise_covariance
+# for the velocity variable in question, or decrease the variance of the variable in question in the measurement
+# itself. In addition, users can also take advantage of the control command being issued to the robot at the time we
+# make the prediction. If control is used, it will get converted into an acceleration term, which will be used during
+# predicition. Note that if an acceleration measurement for the variable in question is available from one of the
+# inputs, the control term will be ignored.
+# Whether or not we use the control input during predicition. Defaults to false.
+use_control: true
+# Whether the input (assumed to be cmd_vel) is a geometry_msgs/Twist or geometry_msgs/TwistStamped message. Defaults to
+# false.
+stamped_control: false
+# The last issued control command will be used in prediction for this period. Defaults to 0.2.
+control_timeout: 0.2
+# Which velocities are being controlled. Order is vx, vy, vz, vroll, vpitch, vyaw.
+control_config: [true, false, false, false, false, true]
+# Places limits on how large the acceleration term will be. Should match your robot's kinematics.
+acceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 3.4]
+# Acceleration and deceleration limits are not always the same for robots.
+deceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 4.5]
+# If your robot cannot instantaneously reach its acceleration limit, the permitted change can be controlled with these
+# gains
+acceleration_gains: [0.8, 0.0, 0.0, 0.0, 0.0, 0.9]
+# If your robot cannot instantaneously reach its deceleration limit, the permitted change can be controlled with these
+# gains
+# deceleration_gains: [1.0, 0.0, 0.0, 0.0, 0.0, 1.0] #-----------------------------------------
+
+
+# [ADVANCED] 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. Defaults to the matrix below if
+# unspecified.
+process_noise_covariance: [0.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0.05, 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]
+
+# [ADVANCED] This represents the initial value for the state estimate error covariance matrix. Setting a diagonal
+# value (variance) to a large value will result in rapid convergence for initial measurements of the variable in
+# question. Users should take care not to use large values for variables that will not be measured directly. The values
+# are ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below
+#if unspecified.
+initial_estimate_covariance: [1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9]
diff --git a/params/ekf2_params.yaml b/params/ekf2_params.yaml
new file mode 100644
index 0000000..89b2f80
--- /dev/null
+++ b/params/ekf2_params.yaml
@@ -0,0 +1,206 @@
+frequency: 20
+
+sensor_timeout: 0.2
+
+two_d_mode: true
+
+# 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
+
+# Use this parameter to provide specify how long the tf listener should wait for a transform to become available.
+# Defaults to 0.0 if unspecified.
+transform_timeout: 0.0
+
+# If you're having trouble, try setting this to true, and then echo the /diagnostics_agg topic to see if the node is
+# unhappy with any settings or data.
+print_diagnostics: true
+
+debug: false
+
+# Defaults to "robot_localization_debug.txt" if unspecified. Please specify the full path.
+debug_out_file: /path/to/debug/file.txt
+
+# Whether to broadcast the transformation over the /tf topic. Defaults to true if unspecified.
+publish_tf: true
+
+# Whether to publish the acceleration state. Defaults to false if unspecified.
+publish_acceleration: false
+
+# REP-105 (http://www.ros.org/reps/rep-0105.html) specifies four principal coordinate frames: base_link, odom, map, and
+# earth. base_link is the coordinate frame that is affixed to the robot. Both odom and map are world-fixed frames.
+# The robot's position in the odom frame will drift over time, but is accurate in the short term and should be
+# continuous. The odom frame is therefore the best frame for executing local motion plans. The map frame, like the odom
+# frame, is a world-fixed coordinate frame, and while it contains the most globally accurate position estimate for your
+# robot, it is subject to discrete jumps, e.g., due to the fusion of GPS data or a correction from a map-based
+# localization node. The earth frame is used to relate multiple map frames by giving them a common reference frame.
+# ekf_localization_node and ukf_localization_node are not concerned with the earth frame.
+# Here is how to use the following settings:
+# 1. Set the map_frame, odom_frame, and base_link frames to the appropriate frame names for your system.
+# 1a. If your system does not have a map_frame, just remove it, and make sure "world_frame" is set to the value of
+# odom_frame.
+# 2. If you are fusing continuous position data such as wheel encoder odometry, visual odometry, or IMU data, set
+# "world_frame" to your odom_frame value. This is the default behavior for robot_localization's state estimation nodes.
+# 3. If you are fusing global absolute position data that is subject to discrete jumps (e.g., GPS or position updates
+# from landmark observations) then:
+# 3a. Set your "world_frame" to your map_frame value
+# 3b. MAKE SURE something else is generating the odom->base_link transform. Note that this can even be another state
+# estimation node from robot_localization! However, that instance should *not* fuse the global data.
+map_frame: /map # Defaults to "map" if unspecified
+odom_frame: rosbot2/odom # Defaults to "odom" if unspecified
+base_link_frame: rosbot2/base_link # Defaults to "base_link" if unspecified
+world_frame: rosbot2/odom # Defaults to the value of odom_frame if unspecified
+
+# The filter accepts an arbitrary number of inputs from each input message type (nav_msgs/Odometry,
+# geometry_msgs/PoseWithCovarianceStamped, geometry_msgs/TwistWithCovarianceStamped,
+# sensor_msgs/Imu). To add an input, 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: rosbot2/odom/wheel
+
+# Each sensor reading updates some or all of the filter's state. These options give you greater control over which
+# values from each measurement are fed to the filter. For example, if you have an odometry message as input, but only
+# want to use its Z position value, then set the entire vector to false, except for the third entry. The order of the
+# values is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Note that not some message types
+# do not provide some of the state variables estimated by the filter. 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.
+odom0_config: [true, true, true,
+ true, true, true,
+ false, false, false,
+ false, false, false,
+ false, false, false]
+
+# If you have high-frequency data or are running with a low frequency parameter value, then you may want to increase
+# the size of the subscription queue so that more measurements are fused.
+odom0_queue_size: 6
+
+# [ADVANCED] Large messages in ROS can exhibit strange behavior when they arrive at a high frequency. This is a result
+# of Nagle's algorithm. This option tells the ROS subscriber to use the tcpNoDelay option, which disables Nagle's
+# algorithm.
+odom0_nodelay: false
+
+# [ADVANCED] When measuring one pose variable with two sensors, a situation can arise in which both sensors under-
+# report their covariances. This can lead to the filter rapidly jumping back and forth between each measurement as they
+# arrive. In these cases, it often makes sense to (a) correct the measurement covariances, or (b) if velocity is also
+# measured by one of the sensors, let one sensor measure pose, and the other velocity. However, doing (a) or (b) isn't
+# always feasible, and so we expose the differential parameter. 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 pose measurements; setting differential to true
+# for twist measurements has no effect.
+odom0_differential: false
+
+# [ADVANCED] When the node starts, if this parameter is true, then the first measurement is treated as a "zero point"
+# for all future measurements. While you can achieve the same effect with the differential paremeter, the key
+# difference is 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: true
+
+# [ADVANCED] If your data is subject to outliers, use these threshold settings, expressed as Mahalanobis distances, to
+# control how far away from the current vehicle state a sensor measurement is permitted to be. Each defaults to
+# numeric_limits::max() if unspecified. It is strongly recommended that these parameters be removed if not
+# required. Data is specified at the level of pose and twist variables, rather than for each variable in isolation.
+# For messages that have both pose and twist data, the parameter specifies to which part of the message we are applying
+# the thresholds.
+#odom0_pose_rejection_threshold: 5
+#odom0_twist_rejection_threshold: 1
+
+# values is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az
+
+#-----------------------------------------
+
+imu0: rosbot2/imu
+imu0_config: [false, false, false,
+ false, false, true,
+ false, false, false,
+ false, false, true,
+ false, false, false]
+imu0_nodelay: false
+imu0_differential: true
+imu0_relative: true
+imu0_queue_size: 4
+imu0_pose_rejection_threshold: 0.8 # Note the difference in parameter names
+imu0_twist_rejection_threshold: 0.8 #
+imu0_linear_acceleration_rejection_threshold: 0.8 #
+
+# [ADVANCED] Some IMUs automatically remove acceleration due to gravity, and others don't. If yours doesn't, please set
+# this to true, and *make sure* your data conforms to REP-103, specifically, that the data is in ENU frame.
+imu0_remove_gravitational_acceleration: true
+
+# [ADVANCED] The EKF and UKF models follow a standard predict/correct cycle. During prediction, if there is no
+# acceleration reference, the velocity at time t+1 is simply predicted to be the same as the velocity at time t. During
+# correction, this predicted value is fused with the measured value to produce the new velocity estimate. This can be
+# problematic, as the final velocity will effectively be a weighted average of the old velocity and the new one. When
+# this velocity is the integrated into a new pose, the result can be sluggish covergence. This effect is especially
+# noticeable with LIDAR data during rotations. To get around it, users can try inflating the process_noise_covariance
+# for the velocity variable in question, or decrease the variance of the variable in question in the measurement
+# itself. In addition, users can also take advantage of the control command being issued to the robot at the time we
+# make the prediction. If control is used, it will get converted into an acceleration term, which will be used during
+# predicition. Note that if an acceleration measurement for the variable in question is available from one of the
+# inputs, the control term will be ignored.
+# Whether or not we use the control input during predicition. Defaults to false.
+use_control: true
+# Whether the input (assumed to be cmd_vel) is a geometry_msgs/Twist or geometry_msgs/TwistStamped message. Defaults to
+# false.
+stamped_control: false
+# The last issued control command will be used in prediction for this period. Defaults to 0.2.
+control_timeout: 0.2
+# Which velocities are being controlled. Order is vx, vy, vz, vroll, vpitch, vyaw.
+control_config: [true, false, false, false, false, true]
+# Places limits on how large the acceleration term will be. Should match your robot's kinematics.
+acceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 3.4]
+# Acceleration and deceleration limits are not always the same for robots.
+deceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 4.5]
+# If your robot cannot instantaneously reach its acceleration limit, the permitted change can be controlled with these
+# gains
+acceleration_gains: [0.8, 0.0, 0.0, 0.0, 0.0, 0.9]
+# If your robot cannot instantaneously reach its deceleration limit, the permitted change can be controlled with these
+# gains
+# deceleration_gains: [1.0, 0.0, 0.0, 0.0, 0.0, 1.0] #-----------------------------------------
+
+
+# [ADVANCED] 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. Defaults to the matrix below if
+# unspecified.
+process_noise_covariance: [0.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0.05, 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]
+
+# [ADVANCED] This represents the initial value for the state estimate error covariance matrix. Setting a diagonal
+# value (variance) to a large value will result in rapid convergence for initial measurements of the variable in
+# question. Users should take care not to use large values for variables that will not be measured directly. The values
+# are ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below
+#if unspecified.
+initial_estimate_covariance: [1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9]