From d77e9623210cd9c84206766c80c29adcbf8a97ae Mon Sep 17 00:00:00 2001 From: Adam Date: Tue, 11 Feb 2020 11:55:39 +0100 Subject: [PATCH] changed ekf params --- launch/rosbot1_ekf.launch | 10 ++ launch/rosbot1_hardware.launch | 2 +- launch/rosbot2_ekf.launch | 10 ++ launch/rosbot2_hardware.launch | 2 +- params/ekf1_params.yaml | 206 +++++++++++++++++++++++++++++++++ params/ekf2_params.yaml | 206 +++++++++++++++++++++++++++++++++ 6 files changed, 434 insertions(+), 2 deletions(-) create mode 100644 launch/rosbot1_ekf.launch create mode 100644 launch/rosbot2_ekf.launch create mode 100644 params/ekf1_params.yaml create mode 100644 params/ekf2_params.yaml 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]