ndt_scan_matcher.param.yaml

/**:
  ros__parameters:
    frame:
      # Vehicle reference frame
      base_frame: "base_link"

      # NDT reference frame
      ndt_base_frame: "ndt_base_link"

      # Map frame
      map_frame: "map"


    sensor_points:
      # Required distance of input sensor points. [m]
      # If the max distance of input sensor points is lower than this value, the scan matching will not be performed.
      required_distance: 10.0


    ndt:
      # The maximum difference between two consecutive
      # transformations in order to consider convergence
      trans_epsilon: 0.01

      # The newton line search maximum step length
      step_size: 0.1

      # The ND voxel grid resolution
      resolution: 2.0

      # The number of iterations required to calculate alignment
      max_iterations: 30

      # Number of threads used for parallel computing
      num_threads: 4

      regularization:
        enable: false

        # Regularization scale factor
        scale_factor: 0.01


    initial_pose_estimation:
      # method: 0=RANDOM_SEARCH(including TPE), 1=GRID_SEARCH
      method: 1

      #
      # Parameters for RANDOM_SEARCH
      #
      # The number of particles to estimate initial pose
      particles_num: 200

      # The number of initial random trials in the TPE (Tree-Structured Parzen Estimator).
      # This value should be equal to or less than 'initial_estimate_particles_num' and more than 0.
      # If it is equal to 'initial_estimate_particles_num', the search will be the same as a full random search.
      n_startup_trials: 20

      #
      # Parameters for GRID_SEARCH
      #
      # The number of grid search
      grid_num_x: 3
      grid_num_y: 3
      grid_num_z: 1
      grid_num_roll: 1
      grid_num_pitch: 1
      grid_num_yaw: 80

      # The range of grid search
      grid_search_range_x: 3.0 # [m] (Search range: -3.0m ~ 3.0m)
      grid_search_range_y: 3.0 # [m] (Search range: -3.0m ~ 3.0m)
      grid_search_range_z: 0.0 # [m]
      grid_search_range_roll: 0.0  # [rad]
      grid_search_range_pitch: 0.0 # [rad]
      grid_search_range_yaw: 3.14159265359 # [rad] (Search range: -pi ~ pi)

    validation:
      # Tolerance of timestamp difference between current time and sensor pointcloud. [sec]
      lidar_topic_timeout_sec: 1.0

      # Tolerance of timestamp difference between initial_pose and sensor pointcloud. [sec]
      initial_pose_timeout_sec: 1.0

      # Tolerance of distance difference between two initial poses used for linear interpolation. [m]
      initial_pose_distance_tolerance_m: 10.0

      # The execution time which means probably NDT cannot matches scans properly. [ms]
      critical_upper_bound_exe_time_ms: 100.0


    score_estimation:
      # Converged param type
      # 0=TRANSFORM_PROBABILITY, 1=NEAREST_VOXEL_TRANSFORMATION_LIKELIHOOD
      converged_param_type: 1

      # If converged_param_type is 0
      # Threshold for deciding whether to trust the estimation result
      converged_param_transform_probability: 3.0

      # If converged_param_type is 1
      # Threshold for deciding whether to trust the estimation result
      converged_param_nearest_voxel_transformation_likelihood: 2.3

      # Scan matching score based on no ground LiDAR scan
      no_ground_points:
        enable: false

        # If lidar_point.z - base_link.z <= this threshold , the point will be removed
        z_margin_for_ground_removal: 0.8


    covariance:
      # The covariance of output pose
      # Note that this covariance matrix is empirically derived
      output_pose_covariance:
        [
          0.0225, 0.0,   0.0,   0.0,      0.0,      0.0,
          0.0,   0.0225, 0.0,   0.0,      0.0,      0.0,
          0.0,   0.0,   0.0225, 0.0,      0.0,      0.0,
          0.0,   0.0,   0.0,   0.000625, 0.0,      0.0,
          0.0,   0.0,   0.0,   0.0,      0.000625, 0.0,
          0.0,   0.0,   0.0,   0.0,      0.0,      0.000625,
        ]

      # 2D Real-time covariance estimation with multiple searches (output_pose_covariance is the minimum value)
      covariance_estimation:
        enable: false

        # Offset arrangement in covariance estimation [m]
        # initial_pose_offset_model_x & initial_pose_offset_model_y must have the same number of elements.
        initial_pose_offset_model_x: [0.0, 0.0, 0.5, -0.5, 1.0, -1.0]
        initial_pose_offset_model_y: [0.5, -0.5, 0.0, 0.0, 0.0, 0.0]


    dynamic_map_loading:
      # Dynamic map loading distance
      update_distance: 20.0

      # Dynamic map loading loading radius
      map_radius: 150.0

      # Radius of input LiDAR range (used for diagnostics of dynamic map loading)
      lidar_radius: 100.0