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276 lines
18 KiB
276 lines
18 KiB
# The frequency, in Hz, at which the filter will output a position estimate. Note that the filter will not begin
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# computation until it receives at least one message from one of the inputs. It will then run continuously at the
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# frequency specified here, regardless of whether it receives more measurements. Defaults to 30 if unspecified.
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frequency: 15
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# The period, in seconds, after which we consider a sensor to have timed out. In this event, we carry out a predict
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# cycle on the EKF without correcting it. This parameter can be thought of as the minimum frequency with which the
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# filter will generate new output. Defaults to 1 / frequency if not specified.
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sensor_timeout: 0.2
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# ekf_localization_node and ukf_localization_node both use a 3D omnidirectional motion model. If this parameter is
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# set to true, no 3D information will be used in your state estimate. Use this if you are operating in a planar
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# environment and want to ignore the effect of small variations in the ground plane that might otherwise be detected
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# by, for example, an IMU. Defaults to false if unspecified.
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two_d_mode: true #true #false
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# Use this parameter to provide an offset to the transform generated by ekf_localization_node. This can be used for
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# future dating the transform, which is required for interaction with some other packages. Defaults to 0.0 if
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# unspecified.
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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.
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# Defaults to 0.0 if unspecified.
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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
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# unhappy with any settings or data.
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print_diagnostics: true
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# Debug settings. Not for the faint of heart. Outputs a ludicrous amount of information to the file specified by
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# debug_out_file. I hope you like matrices! Please note that setting this to true will have strongly deleterious
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# effects on the performance of the node. Defaults to false if unspecified.
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debug: false
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# Defaults to "robot_localization_debug.txt" if unspecified. Please specify the full path.
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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.
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publish_tf: false #true
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# Whether to publish the acceleration state. Defaults to false if unspecified.
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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
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# earth. base_link is the coordinate frame that is affixed to the robot. Both odom and map are world-fixed frames.
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# The robot's position in the odom frame will drift over time, but is accurate in the short term and should be
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# continuous. The odom frame is therefore the best frame for executing local motion plans. The map frame, like the odom
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# frame, is a world-fixed coordinate frame, and while it contains the most globally accurate position estimate for your
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# robot, it is subject to discrete jumps, e.g., due to the fusion of GPS data or a correction from a map-based
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# localization node. The earth frame is used to relate multiple map frames by giving them a common reference frame.
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# ekf_localization_node and ukf_localization_node are not concerned with the earth frame.
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# Here is how to use the following settings:
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# 1. Set the map_frame, odom_frame, and base_link frames to the appropriate frame names for your system.
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# 1a. If your system does not have a map_frame, just remove it, and make sure "world_frame" is set to the value of
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# odom_frame.
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# 2. If you are fusing continuous position data such as wheel encoder odometry, visual odometry, or IMU data, set
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# "world_frame" to your odom_frame value. This is the default behavior for robot_localization's state estimation nodes.
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# 3. If you are fusing global absolute position data that is subject to discrete jumps (e.g., GPS or position updates
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# from landmark observations) then:
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# 3a. Set your "world_frame" to your map_frame value
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# 3b. MAKE SURE something else is generating the odom->base_link transform. Note that this can even be another state
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# estimation node from robot_localization! However, that instance should *not* fuse the global data.
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map_frame: map # Defaults to "map" if unspecified
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odom_frame: odom # Defaults to "odom" if unspecified
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base_link_frame: base_link # Defaults to "base_link" if unspecified
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world_frame: 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,
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# geometry_msgs/PoseWithCovarianceStamped, geometry_msgs/TwistWithCovarianceStamped,
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# sensor_msgs/Imu). To add an input, simply append the next number in the sequence to its "base" name, e.g., odom0,
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# odom1, twist0, twist1, imu0, imu1, imu2, etc. The value should be the topic name. These parameters obviously have no
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# default values, and must be specified.
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odom0: /odom #/wheel/odom
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# Each sensor reading updates some or all of the filter's state. These options give you greater control over which
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# values from each measurement are fed to the filter. For example, if you have an odometry message as input, but only
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# want to use its Z position value, then set the entire vector to false, except for the third entry. The order of the
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# values is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Note that not some message types
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# do not provide some of the state variables estimated by the filter. For example, a TwistWithCovarianceStamped message
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# has no pose information, so the first six values would be meaningless in that case. Each vector defaults to all false
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# if unspecified, effectively making this parameter required for each sensor.
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# odom0_config: [true, true, false,
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# false, false, false,
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# false, false, false,
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# false, false, true,
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# false, false, false]
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# odom0_config: [true, true, true,
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# false, false, false,
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# false, false, false,
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# false, false, false,
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# false, false, false]
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odom0_config: [true, true, true,
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false, false, true,
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false, false, false,
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false, false, false,
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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
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# the size of the subscription queue so that more measurements are fused.
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odom0_queue_size: 1 #2
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# [ADVANCED] Large messages in ROS can exhibit strange behavior when they arrive at a high frequency. This is a result
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# of Nagle's algorithm. This option tells the ROS subscriber to use the tcpNoDelay option, which disables Nagle's
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# algorithm.
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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-
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# report their covariances. This can lead to the filter rapidly jumping back and forth between each measurement as they
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# arrive. In these cases, it often makes sense to (a) correct the measurement covariances, or (b) if velocity is also
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# measured by one of the sensors, let one sensor measure pose, and the other velocity. However, doing (a) or (b) isn't
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# always feasible, and so we expose the differential parameter. When differential mode is enabled, all absolute pose
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# data is converted to velocity data by differentiating the absolute pose measurements. These velocities are then
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# integrated as usual. NOTE: this only applies to sensors that provide pose measurements; setting differential to true
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# for twist measurements has no effect.
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odom0_differential: true
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# [ADVANCED] When the node starts, if this parameter is true, then the first measurement is treated as a "zero point"
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# for all future measurements. While you can achieve the same effect with the differential paremeter, the key
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# difference is that the relative parameter doesn't cause the measurement to be converted to a velocity before
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# integrating it. If you simply want your measurements to start at 0 for a given sensor, set this to true.
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odom0_relative: false
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# [ADVANCED] If your data is subject to outliers, use these threshold settings, expressed as Mahalanobis distances, to
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# control how far away from the current vehicle state a sensor measurement is permitted to be. Each defaults to
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# numeric_limits<double>::max() if unspecified. It is strongly recommended that these parameters be removed if not
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# required. Data is specified at the level of pose and twist variables, rather than for each variable in isolation.
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# For messages that have both pose and twist data, the parameter specifies to which part of the message we are applying
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# the thresholds.
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#odom0_pose_rejection_threshold: 5
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#odom0_twist_rejection_threshold: 1
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# Further input parameter examples
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# odom1: example/another_odom
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# odom1_config: [false, false, true,
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# false, false, false,
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# false, false, false,
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# false, false, true,
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# false, false, false]
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# odom1_differential: false
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# odom1_relative: true
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# odom1_queue_size: 2
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# odom1_pose_rejection_threshold: 2
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# odom1_twist_rejection_threshold: 0.2
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# odom1_nodelay: false
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# pose0: example/pose
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# pose0_config: [true, true, false,
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# false, false, false,
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# false, false, false,
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# false, false, false,
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# false, false, false]
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# pose0_differential: true
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# pose0_relative: false
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# pose0_queue_size: 5
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# pose0_rejection_threshold: 2 # Note the difference in parameter name
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# pose0_nodelay: false
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# twist0: example/twist
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# twist0_config: [false, false, false,
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# false, false, false,
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# true, true, true,
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# false, false, false,
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# false, false, false]
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# twist0_queue_size: 3
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# twist0_rejection_threshold: 2
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# twist0_nodelay: false
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# Each sensor reading updates some or all of the filter's state. These options give you greater control over which
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# values from each measurement are fed to the filter. For example, if you have an odometry message as input, but only
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# want to use its Z position value, then set the entire vector to false, except for the third entry. The order of the
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# values is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Note that not some message types
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# do not provide some of the state variables estimated by the filter. For example, a TwistWithCovarianceStamped message
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# has no pose information, so the first six values would be meaningless in that case. Each vector defaults to all false
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# if unspecified, effectively making this parameter required for each sensor.
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imu0: /imu/data
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#imu0_config: [false, false, false,
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# true, true, true,
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# false, false, false,
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# true, true, true,
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# true, true, true]
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imu0_config: [false, false, false,
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false, false, true,
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false, false, false,
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false, false, true,
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false, false, false]
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imu0_nodelay: false
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imu0_differential: true #false
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imu0_relative: true
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imu0_queue_size: 1 #5
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#imu0_pose_rejection_threshold: 0.8 # Note the difference in parameter names
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#imu0_twist_rejection_threshold: 0.8 #
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#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
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# this to true, and *make sure* your data conforms to REP-103, specifically, that the data is in ENU frame.
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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
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# acceleration reference, the velocity at time t+1 is simply predicted to be the same as the velocity at time t. During
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# correction, this predicted value is fused with the measured value to produce the new velocity estimate. This can be
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# problematic, as the final velocity will effectively be a weighted average of the old velocity and the new one. When
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# this velocity is the integrated into a new pose, the result can be sluggish covergence. This effect is especially
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# noticeable with LIDAR data during rotations. To get around it, users can try inflating the process_noise_covariance
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# for the velocity variable in question, or decrease the variance of the variable in question in the measurement
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# itself. In addition, users can also take advantage of the control command being issued to the robot at the time we
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# make the prediction. If control is used, it will get converted into an acceleration term, which will be used during
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# predicition. Note that if an acceleration measurement for the variable in question is available from one of the
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# inputs, the control term will be ignored.
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# Whether or not we use the control input during predicition. Defaults to false.
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use_control: false #true
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# Whether the input (assumed to be cmd_vel) is a geometry_msgs/Twist or geometry_msgs/TwistStamped message. Defaults to
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# false.
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stamped_control: false
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# The last issued control command will be used in prediction for this period. Defaults to 0.2.
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control_timeout: 0.2
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# Which velocities are being controlled. Order is vx, vy, vz, vroll, vpitch, vyaw.
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control_config: [true, false, false, false, false, true]
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# Places limits on how large the acceleration term will be. Should match your robot's kinematics.
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acceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 3.4]
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# Acceleration and deceleration limits are not always the same for robots.
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deceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 4.5]
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# If your robot cannot instantaneously reach its acceleration limit, the permitted change can be controlled with these
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# gains
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acceleration_gains: [0.8, 0.0, 0.0, 0.0, 0.0, 0.9]
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# If your robot cannot instantaneously reach its deceleration limit, the permitted change can be controlled with these
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# gains
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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
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# exposed as a configuration parameter. This matrix represents the noise we add to the total error after each
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# prediction step. The better the omnidirectional motion model matches your system, the smaller these values can be.
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# However, if users find that a given variable is slow to converge, one approach is to increase the
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# process_noise_covariance diagonal value for the variable in question, which will cause the filter's predicted error
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# to be larger, which will cause the filter to trust the incoming measurement more during correction. The values are
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# ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below if
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# unspecified.
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process_noise_covariance: [0.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0.06, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0.06, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0.025, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0.025, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0,
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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
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# value (variance) to a large value will result in rapid convergence for initial measurements of the variable in
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# question. Users should take care not to use large values for variables that will not be measured directly. The values
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# are ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below
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#if unspecified.
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initial_estimate_covariance: [1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9]
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