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I have multiple odometry sensors that is attached to my robot on different parts of the robot. I want to use these odometry values to be fed into the robot_localization package in ros2 humble. I tried to run the ukf.launch.py. This is my ukf.yaml file :

### ukf config file ###
ukf_filter_node:
    ros__parameters:
        # The frequency, in Hz, at which the filter will output a position estimate. Note that the filter will not begin
        # computation until it receives at least one message from one of the inputs. It will then run continuously at the
        # frequency specified here, regardless of whether it receives more measurements. Defaults to 30 if unspecified.
        frequency: 30.0

    # The period, in seconds, after which we consider a sensor to have timed out. In this event, we carry out a predict
    # cycle on the EKF without correcting it. This parameter can be thought of as the minimum frequency with which the
    # filter will generate new output. Defaults to 1 / frequency if not specified.
    sensor_timeout: 0.1

    # ekf_localization_node and ukf_localization_node both use a 3D omnidirectional motion model. If this parameter is
    # set to true, no 3D information will be used in your state estimate. Use this if you are operating in a planar
    # environment and want to ignore the effect of small variations in the ground plane that might otherwise be detected
    # by, for example, an IMU. Defaults to false if unspecified.
    two_d_mode: false

    # 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 settings. Not for the faint of heart. Outputs a ludicrous amount of information to the file specified by
    # debug_out_file. I hope you like matrices! Please note that setting this to true will have strongly deleterious
    # effects on the performance of the node. Defaults to false if unspecified.
    debug: false

    # Defaults to "robot_localization_debug.txt" if unspecified. Please specify the full path.
    debug_out_file: /path/to/debug/file.txt

    # Whether we'll allow old measurements to cause a re-publication of the updated state
    permit_corrected_publication: false

    # Whether to publish the acceleration state. Defaults to false if unspecified.
    publish_acceleration: false

    # Whether to broadcast the transformation over the /tf topic. Defaults to true if unspecified.
    publish_tf: true

    # 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: odom            # Defaults to "odom" if unspecified
    base_link_frame: base_link  # Defaults to "base_link" if unspecified
    world_frame: 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: /odometry0
    # 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,
                   false, false, false,
                   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: 2

    # [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: false

    # [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: 5.0
    odom0_twist_rejection_threshold: 1.0

    # Further input parameter examples
    odom1: /odometry1
    odom1_config: [true, true, true,
                   false, false, false,
                   false, false, false,
                   false, false, false,
                   false, false, false]
    odom1_differential: false
    odom1_relative: false
    odom1_queue_size: 2
    odom1_pose_rejection_threshold: 5.0
    odom1_twist_rejection_threshold: 1.0


    # Further input parameter examples
    odom2: /odometry2
    odom2_config: [true, true, true,
                   false, false, false,
                   false, false, false,
                   false, false, false,
                   false, false, false]
    odom2_differential: false
    odom2_relative: false
    odom2_queue_size: 2
    odom2_pose_rejection_threshold: 5.0
    odom2_twist_rejection_threshold: 1.0

    # Further input parameter examples
    odom3: /odometry3
    odom3_config: [true, true, true,
                   false, false, false,
                   false, false, false,
                   false, false, false,
                   false, false, false]
    odom3_differential: false
    odom3_relative: false
    odom3_queue_size: 2
    odom3_pose_rejection_threshold: 5.0
    odom3_twist_rejection_threshold: 1.0


    # Further input parameter examples
    odom4: /odometry4
    odom4_config: [true, true, true,
                   false, false, false,
                   false, false, false,
                   false, false, false,
                   false, false, false]
    odom4_differential: false
    odom4_relative: false
    odom4_queue_size: 2
    odom4_pose_rejection_threshold: 5.0
    odom4_twist_rejection_threshold: 1.0


    # Further input parameter examples
    odom5: /odometry5
    odom5_config: [true, true, true,
                   false, false, false,
                   false, false, false,
                   false, false, false,
                   false, false, false]
    odom5_differential: false
    odom5_relative: false
    odom5_queue_size: 2
    odom5_pose_rejection_threshold: 5.0
    odom5_twist_rejection_threshold: 1.0



    imu0: example/imu
    imu0_config: [false, false, false,
                  true,  true,  true,
                  false, false, false,
                  true,  true,  true,
                  true,  true,  true]
    imu0_differential: false
    imu0_relative: true
    imu0_queue_size: 5
    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.
    # Note: the specification of covariance matrices can be cumbersome, so all matrix parameters in this package support
    # both full specification or specification of only the diagonal values.
    process_noise_covariance: [0.05, 0.0,    0.0,    0.0,    0.0,    0.0,    0.0,     0.0,     0.0,    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,    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,    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,    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,    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,    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,
                               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,    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,    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,    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,    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,    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,    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,     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 diagonal values below
    # if unspecified. In this example, we specify only the diagonal of the matrix.
    initial_estimate_covariance: [1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9]

    # [ADVANCED, UKF ONLY] The alpha and kappa variables control the spread of the sigma points. Unless you are familiar
    # with UKFs, it's probably a good idea to leave these alone.
    # Defaults to 0.001 if unspecified.
    alpha: 0.001
    # Defaults to 0 if unspecified.
    kappa: 0.0

    # [ADVANCED, UKF ONLY] The beta variable relates to the distribution of the state vector. Again, it's probably best to
    # leave this alone if you're uncertain. Defaults to 2 if unspecified.
    beta: 2.0

When I launch the ukf.launch.py, I got the error

Error:   TF_DENORMALIZED_QUATERNION: Ignoring transform for child_frame_id "base_link" from authority "default_authority" because of an invalid quaternion in the transform (-nan -nan -nan -nan)
         at line 254 in ./src/buffer_core.cpp

I then found the solution to solve the issue from https://answers.ros.org/question/411025/what-should-be-published-in-clock-when-using-a-real-robot/

The question that I want to ask you guys is how can i set the transform between the base_link and the odom frame as i have several odometry sensors ? Do you guys have your thought about this ?

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  • $\begingroup$ Please remove all the comments from the config file. It makes it harder to read, and just contains all the comments from the template. $\endgroup$
    – automatom
    Commented Feb 15 at 16:43
  • $\begingroup$ Also, Nobel's answer is correct. Can you please accept it? $\endgroup$
    – automatom
    Commented Feb 16 at 10:15

1 Answer 1

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The robot_localization package is very well-documented in the wiki You can see that the package will publish its own odom topic called /odometry/filtered. The package also will publish the transform between the odom frame and the base_link. This can be configured in the YAML file if needed. So normally I should work directly out of the box.

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