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tier4-autoware-public-bot[bot]tier4-autoware-public-bot[bot]
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Merge pull request #298 from tier4/sync-awf-latest
chore: sync awf-latest
2 parents bed643c + 14d144c commit 0fd518f

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‎autoware_launch/config/localization/ndt_scan_matcher.param.yaml

+85-73
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@@ -1,102 +1,114 @@
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/**:
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ros__parameters:
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# Vehicle reference frame
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base_frame: "base_link"
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frame:
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# Vehicle reference frame
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base_frame: "base_link"
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# NDT reference frame
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ndt_base_frame: "ndt_base_link"
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# NDT reference frame
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ndt_base_frame: "ndt_base_link"
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# map frame
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map_frame: "map"
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# Map frame
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map_frame: "map"
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# Subscriber queue size
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input_sensor_points_queue_size: 1
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# The maximum difference between two consecutive
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# transformations in order to consider convergence
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trans_epsilon: 0.01
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ndt:
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# The maximum difference between two consecutive
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# transformations in order to consider convergence
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trans_epsilon: 0.01
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# The newton line search maximum step length
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step_size: 0.1
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# The newton line search maximum step length
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step_size: 0.1
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# The ND voxel grid resolution
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resolution: 2.0
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# The ND voxel grid resolution
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resolution: 2.0
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# The number of iterations required to calculate alignment
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max_iterations: 30
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# The number of iterations required to calculate alignment
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max_iterations: 30
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# Converged param type
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# 0=TRANSFORM_PROBABILITY, 1=NEAREST_VOXEL_TRANSFORMATION_LIKELIHOOD
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converged_param_type: 1
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# Number of threads used for parallel computing
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num_threads: 4
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# If converged_param_type is 0
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# Threshold for deciding whether to trust the estimation result
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converged_param_transform_probability: 3.0
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regularization:
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enable: false
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# If converged_param_type is 1
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# Threshold for deciding whether to trust the estimation result
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converged_param_nearest_voxel_transformation_likelihood: 2.3
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# Regularization scale factor
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scale_factor: 0.01
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# The number of particles to estimate initial pose
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initial_estimate_particles_num: 200
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# The number of initial random trials in the TPE (Tree-Structured Parzen Estimator).
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# This value should be equal to or less than 'initial_estimate_particles_num' and more than 0.
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# If it is equal to 'initial_estimate_particles_num', the search will be the same as a full random search.
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n_startup_trials: 20
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initial_pose_estimation:
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# The number of particles to estimate initial pose
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particles_num: 200
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# Tolerance of timestamp difference between current time and sensor pointcloud. [sec]
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lidar_topic_timeout_sec: 1.0
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# The number of initial random trials in the TPE (Tree-Structured Parzen Estimator).
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# This value should be equal to or less than 'initial_estimate_particles_num' and more than 0.
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# If it is equal to 'initial_estimate_particles_num', the search will be the same as a full random search.
45+
n_startup_trials: 20
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# Tolerance of timestamp difference between initial_pose and sensor pointcloud. [sec]
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initial_pose_timeout_sec: 1.0
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# Tolerance of distance difference between two initial poses used for linear interpolation. [m]
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initial_pose_distance_tolerance_m: 10.0
48+
validation:
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# Tolerance of timestamp difference between current time and sensor pointcloud. [sec]
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lidar_topic_timeout_sec: 1.0
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57-
# The execution time which means probably NDT cannot matches scans properly. [ms]
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critical_upper_bound_exe_time_ms: 100.0
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# Tolerance of timestamp difference between initial_pose and sensor pointcloud. [sec]
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initial_pose_timeout_sec: 1.0
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60-
# Number of threads used for parallel computing
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num_threads: 4
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# Tolerance of distance difference between two initial poses used for linear interpolation. [m]
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initial_pose_distance_tolerance_m: 10.0
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# The covariance of output pose
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# Note that this covariance matrix is empirically derived
65-
output_pose_covariance:
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[
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0.0225, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0225, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0225, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.000625, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.000625, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.0, 0.000625,
73-
]
58+
# The execution time which means probably NDT cannot matches scans properly. [ms]
59+
critical_upper_bound_exe_time_ms: 100.0
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# 2D Real-time covariance estimation with multiple searches (output_pose_covariance is the minimum value)
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use_covariance_estimation: false
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# Offset arrangement in covariance estimation [m]
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# initial_pose_offset_model_x & initial_pose_offset_model_y must have the same number of elements.
80-
initial_pose_offset_model_x: [0.0, 0.0, 0.5, -0.5, 1.0, -1.0]
81-
initial_pose_offset_model_y: [0.5, -0.5, 0.0, 0.0, 0.0, 0.0]
62+
score_estimation:
63+
# Converged param type
64+
# 0=TRANSFORM_PROBABILITY, 1=NEAREST_VOXEL_TRANSFORMATION_LIKELIHOOD
65+
converged_param_type: 1
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83-
# Regularization switch
84-
regularization_enabled: false
67+
# If converged_param_type is 0
68+
# Threshold for deciding whether to trust the estimation result
69+
converged_param_transform_probability: 3.0
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# Regularization scale factor
87-
regularization_scale_factor: 0.01
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# If converged_param_type is 1
72+
# Threshold for deciding whether to trust the estimation result
73+
converged_param_nearest_voxel_transformation_likelihood: 2.3
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# Dynamic map loading distance
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dynamic_map_loading_update_distance: 20.0
75+
# Scan matching score based on no ground LiDAR scan
76+
no_ground_points:
77+
enable: false
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# Dynamic map loading loading radius
93-
dynamic_map_loading_map_radius: 150.0
79+
# If lidar_point.z - base_link.z <= this threshold , the point will be removed
80+
z_margin_for_ground_removal: 0.8
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# Radius of input LiDAR range (used for diagnostics of dynamic map loading)
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lidar_radius: 100.0
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# A flag for using scan matching score based on no ground LiDAR scan
99-
estimate_scores_by_no_ground_points: false
83+
covariance:
84+
# The covariance of output pose
85+
# Note that this covariance matrix is empirically derived
86+
output_pose_covariance:
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[
88+
0.0225, 0.0, 0.0, 0.0, 0.0, 0.0,
89+
0.0, 0.0225, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0225, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.000625, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.000625, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.0, 0.000625,
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]
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# If lidar_point.z - base_link.z <= this threshold , the point will be removed
102-
z_margin_for_ground_removal: 0.8
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# 2D Real-time covariance estimation with multiple searches (output_pose_covariance is the minimum value)
97+
covariance_estimation:
98+
enable: false
99+
100+
# Offset arrangement in covariance estimation [m]
101+
# initial_pose_offset_model_x & initial_pose_offset_model_y must have the same number of elements.
102+
initial_pose_offset_model_x: [0.0, 0.0, 0.5, -0.5, 1.0, -1.0]
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initial_pose_offset_model_y: [0.5, -0.5, 0.0, 0.0, 0.0, 0.0]
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105+
106+
dynamic_map_loading:
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# Dynamic map loading distance
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update_distance: 20.0
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# Dynamic map loading loading radius
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map_radius: 150.0
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# Radius of input LiDAR range (used for diagnostics of dynamic map loading)
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lidar_radius: 100.0

‎autoware_launch/config/localization/twist2accel.param.yaml

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/**:
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ros__parameters:
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use_odom: true
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accel_lowpass_gain: 0.9

‎autoware_launch/config/map/map_projection_loader.param.yaml

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/**:
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ros__parameters:
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map_projector_info_path: $(var map_projector_info_path)
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lanelet2_map_path: $(var lanelet2_map_path)

‎autoware_launch/config/perception/object_recognition/detection/lidar_model/detection_class_remapper.param.yaml

+3-3
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@@ -29,9 +29,9 @@
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max_area_matrix:
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#UNKNOWN, CAR, TRUCK, BUS, TRAILER, MOTORBIKE, BICYCLE, PEDESTRIAN
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[ 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #UNKNOWN
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0.000, 0.000, 36.000, 0.000, inf, 0.000, 0.000, 0.000, #CAR
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0.000, 0.000, 0.000, 0.000, inf, 0.000, 0.000, 0.000, #TRUCK
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0.000, 0.000, 0.000, 0.000, inf, 0.000, 0.000, 0.000, #BUS
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0.000, 0.000, 36.000, 0.000, 999.999, 0.000, 0.000, 0.000, #CAR
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0.000, 0.000, 0.000, 0.000, 999.999, 0.000, 0.000, 0.000, #TRUCK
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0.000, 0.000, 0.000, 0.000, 999.999, 0.000, 0.000, 0.000, #BUS
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0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #TRAILER
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0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #MOTORBIKE
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0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #BICYCLE

‎autoware_launch/config/perception/object_recognition/detection/radar_crossing_objects_noise_filter/radar_crossing_objects_noise_filter.param.yaml

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/**:
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ros__parameters:
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angle_threshold: 1.2210
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velocity_threshold: 1.5

‎autoware_launch/launch/components/tier4_localization_component.launch.xml

+1
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@@ -25,6 +25,7 @@
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<arg name="localization_error_monitor_param_path" value="$(var loc_config_path)/localization_error_monitor.param.yaml"/>
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<arg name="ekf_localizer_param_path" value="$(var loc_config_path)/ekf_localizer.param.yaml"/>
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<arg name="pose_initializer_param_path" value="$(var loc_config_path)/pose_initializer.param.yaml"/>
28+
<arg name="twist2accel_param_path" value="$(var loc_config_path)/twist2accel.param.yaml"/>
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2930
<!-- parameter paths for ndt -->
3031
<arg name="ndt_scan_matcher_param_path" value="$(var loc_config_path)/ndt_scan_matcher.param.yaml"/>

‎autoware_launch/launch/components/tier4_map_component.launch.xml

+1
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@@ -8,5 +8,6 @@
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<arg name="pointcloud_map_loader_param_path" value="$(find-pkg-share autoware_launch)/config/map/pointcloud_map_loader.param.yaml"/>
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<arg name="lanelet2_map_loader_param_path" value="$(find-pkg-share autoware_launch)/config/map/lanelet2_map_loader.param.yaml"/>
11+
<arg name="map_projection_loader_param_path" value="$(find-pkg-share autoware_launch)/config/map/map_projection_loader.param.yaml"/>
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</include>
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</launch>

‎autoware_launch/launch/components/tier4_perception_component.launch.xml

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@@ -80,7 +80,11 @@
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value="$(find-pkg-share autoware_launch)/config/perception/object_recognition/detection/detection_by_tracker/detection_by_tracker.param.yaml"
8181
/>
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<arg
83-
name="object_recognition_detection_radar_lanelet_filtering_range_param"
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name="object_recognition_detection_radar_crossing_objects_noise_filter_param_path"
84+
value="$(find-pkg-share autoware_launch)/config/perception/object_recognition/detection/radar_crossing_objects_noise_filter/radar_crossing_objects_noise_filter.param.yaml"
85+
/>
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<arg
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name="object_recognition_detection_radar_lanelet_filtering_range_param_path"
8488
value="$(find-pkg-share autoware_launch)/config/perception/object_recognition/detection/object_filter/radar_lanelet_filter.param.yaml"
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/>
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<arg

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