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<launch> | ||
<node pkg="robot_localization" type="ekf_localization_node" name="rosbot_ekf" clear_params="true"> | ||
<rosparam command="load" file="$(find multiple_rosbots)/params/ekf1_params.yaml" /> | ||
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<!-- Placeholder for output topic remapping --> | ||
<remap from="odometry/filtered" to="rosbot1/odom"/> | ||
<!-- <remap from="accel/filtered" to=""/> --> | ||
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</node> | ||
</launch> |
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<launch> | ||
<node pkg="robot_localization" type="ekf_localization_node" name="rosbot_ekf" clear_params="true"> | ||
<rosparam command="load" file="$(find multiple_rosbots)/params/ekf2_params.yaml" /> | ||
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<!-- Placeholder for output topic remapping --> | ||
<remap from="odometry/filtered" to="rosbot2/odom"/> | ||
<!-- <remap from="accel/filtered" to=""/> --> | ||
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</node> | ||
</launch> |
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frequency: 20 | ||
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sensor_timeout: 0.2 | ||
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two_d_mode: true | ||
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# 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 | ||
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# 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 | ||
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# 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 | ||
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debug: false | ||
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# Defaults to "robot_localization_debug.txt" if unspecified. Please specify the full path. | ||
debug_out_file: /path/to/debug/file.txt | ||
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# Whether to broadcast the transformation over the /tf topic. Defaults to true if unspecified. | ||
publish_tf: true | ||
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# Whether to publish the acceleration state. Defaults to false if unspecified. | ||
publish_acceleration: false | ||
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# 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 | ||
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# 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 | ||
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# 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] | ||
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# 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 | ||
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# [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 | ||
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# [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 | ||
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# [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 | ||
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# [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<double>::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 | ||
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# values is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az | ||
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#----------------------------------------- | ||
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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 # | ||
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# [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 | ||
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# [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] #----------------------------------------- | ||
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# [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] | ||
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# [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] |
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