Scattering behavior of Zero 3

[talari-bgu]

Hi,

Im using Zero 3 model with a NUC i7, running Ubuntu 22.04 and ROS2 Humble.
I followed the instructions to install all the necessary packages and it works pretty well. The only issue I'm having is when I run slam_launch.py; the robot starts acting up when I rotate it, just like in the video. I figured out that the trouble seems to be linked to odometry/filtered because when I turn it off, the robot stops acting weird. But turning off odometry/filtered isn't really a solution for me.

Best Regards

Screencast.from.05-15-2024.12_02_44.PM.webm

[giovanidiniz]

@talari-bgu I suspect you have two nodes publishing to the same TF.
if you can post your slam and ekf configs here I can take a look for you.

[talari-bgu]

Hi @giovanidiniz.
Worth to mention that the slam node in rviz2 works fine when I run the robot in Gazebo.

ekf_filter_node:

    ros__parameters:
        use_sim_time: false
        frequency: 15.0
        sensor_timeout: 1.0
        two_d_mode: true
        transform_time_offset: 0.0
        transform_timeout: 0.0
        print_diagnostics: true
        debug: false
        debug_out_file: /path/to/debug/file.txt
        publish_tf: true
        publish_acceleration: false
        reset_on_time_jump: true

        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
        odom0: odometry/wheels

        odom0_config: [false, false, false,
                       false, false, false,
                       true,  true,  false,
                       false, false, false,
                       false, false, false]

        #        [x_pos   , y_pos    , z_pos,
        #         roll    , pitch    , yaw,
        #         x_vel   , y_vel    , z_vel,
        #         roll_vel, pitch_vel, yaw_vel,
        #         x_accel , y_accel  , z_accel]

        odom0_queue_size: 10
        odom0_nodelay: true
        odom0_differential: false
        odom0_relative: true

        imu0: imu/data
        imu0_config: [false, false, false,
                      false,  false,  false,
                      false, false, false,
                      false,  false,  true,
                      false,  false,  false]

        #        [x_pos   , y_pos    , z_pos,
        #         roll    , pitch    , yaw,
        #         x_vel   , y_vel    , z_vel,
        #         roll_vel, pitch_vel, yaw_vel,
        #         x_accel , y_accel  , z_accel]
        
        imu0_nodelay: true
        imu0_differential: false
        imu0_relative: true
        imu0_queue_size: 12

        imu0_remove_gravitational_acceleration: true

        use_control: false
        stamped_control: false
        control_timeout: 0.2
        control_config: [true, false, false, false, false, true]
        acceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 3.4]
        deceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 4.5]
        acceleration_gains: [0.8, 0.0, 0.0, 0.0, 0.0, 0.9]
        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: [0.1,   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,    0.1,   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,    0.2,   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,     0.05,   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,     0.04,   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,    0.5,   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,    0.4,   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 matrix below
#if unspecified.
        initial_estimate_covariance: [1e-9,   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,    1e-9,   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,    1e-9,   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,    1e-9,   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,    1e-9,   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,    1e-9,   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,    0.1,   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,    0.1,   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,    1e-9,   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,    1e-9,    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,     1e-9,    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,     0.1,    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,     1e-9,   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,    1e-9,   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,    1e-9]

slam_toolbox:

  ros__parameters:

    # Plugin params
    solver_plugin: solver_plugins::CeresSolver
    ceres_linear_solver: SPARSE_NORMAL_CHOLESKY
    ceres_preconditioner: SCHUR_JACOBI
    ceres_trust_strategy: LEVENBERG_MARQUARDT
    ceres_dogleg_type: TRADITIONAL_DOGLEG
    ceres_loss_function: None

    # ROS Parameters
    odom_frame: odom
    map_frame: map
    base_frame: base_link
    scan_topic: /scan
    mode: mapping

    # if you'd like to immediately start continuing a map at a given pose
    # or at the dock, but they are mutually exclusive, if pose is given
    # will use pose
    #map_file_name: test_steve
    # map_start_pose: [0.0, 0.0, 0.0]
    #map_start_at_dock: true
    #map_file_name: /home/rover/rover_workspace/office_map
    #map_start_pose: [0.0, 0.0, 0.0]

    debug_logging: false
    throttle_scans: 1
    transform_publish_period: 0.02 #if 0 never publishes odometry
    map_update_interval: 5.0
    resolution: 0.05
    max_laser_range: 20.0 #for rastering images
    minimum_time_interval: 0.5
    transform_timeout: 0.2
    tf_buffer_duration: 30.
    stack_size_to_use: 40000000 #// program needs a larger stack size to serialize large maps
    enable_interactive_mode: true

    # General Parameters
    use_scan_matching: true
    use_scan_barycenter: true
    minimum_travel_distance: 0.25
    minimum_travel_heading: 0.25
    scan_buffer_size: 5
    scan_buffer_maximum_scan_distance: 10.0
    link_match_minimum_response_fine: 0.1  
    link_scan_maximum_distance: 1.5
    loop_search_maximum_distance: 3.0
    do_loop_closing: true 
    loop_match_minimum_chain_size: 10           
    loop_match_maximum_variance_coarse: 3.0  
    loop_match_minimum_response_coarse: 0.35    
    loop_match_minimum_response_fine: 0.45

    # Correlation Parameters - Correlation Parameters
    correlation_search_space_dimension: 0.5
    correlation_search_space_resolution: 0.01
    correlation_search_space_smear_deviation: 0.1 

    # Correlation Parameters - Loop Closure Parameters
    loop_search_space_dimension: 8.0
    loop_search_space_resolution: 0.05
    loop_search_space_smear_deviation: 0.03

    # Scan Matcher Parameters
    distance_variance_penalty: 0.5      
    angle_variance_penalty: 1.0    

    fine_search_angle_offset: 0.00349     
    coarse_search_angle_offset: 0.349   
    coarse_angle_resolution: 0.0349        
    minimum_angle_penalty: 0.9
    minimum_distance_penalty: 0.5
    use_response_expansion: true