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<description>The object_recognition_utils package</description> | ||
<maintainer email="[email protected]">Takayuki Murooka</maintainer> | ||
<maintainer email="[email protected]">Satoshi Tanaka</maintainer> | ||
<maintainer email="[email protected]">Yusuke Muramatsu</maintainer> | ||
<maintainer email="[email protected]">Shunsuke Miura</maintainer> | ||
<maintainer email="[email protected]">Yoshi Ri</maintainer> | ||
<license>Apache License 2.0</license> | ||
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<version>0.1.0</version> | ||
<description>The perception_utils package</description> | ||
<maintainer email="[email protected]">Satoshi Tanaka</maintainer> | ||
<maintainer email="[email protected]">Yusuke Muramatsu</maintainer> | ||
<maintainer email="[email protected]">Shunsuke Miura</maintainer> | ||
<maintainer email="[email protected]">Yoshi Ri</maintainer> | ||
<license>Apache License 2.0</license> | ||
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158 changes: 85 additions & 73 deletions
158
localization/ndt_scan_matcher/config/ndt_scan_matcher.param.yaml
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/**: | ||
ros__parameters: | ||
# Vehicle reference frame | ||
base_frame: "base_link" | ||
frame: | ||
# Vehicle reference frame | ||
base_frame: "base_link" | ||
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# NDT reference frame | ||
ndt_base_frame: "ndt_base_link" | ||
# NDT reference frame | ||
ndt_base_frame: "ndt_base_link" | ||
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# map frame | ||
map_frame: "map" | ||
# Map frame | ||
map_frame: "map" | ||
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# Subscriber queue size | ||
input_sensor_points_queue_size: 1 | ||
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# The maximum difference between two consecutive | ||
# transformations in order to consider convergence | ||
trans_epsilon: 0.01 | ||
ndt: | ||
# The maximum difference between two consecutive | ||
# transformations in order to consider convergence | ||
trans_epsilon: 0.01 | ||
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# The newton line search maximum step length | ||
step_size: 0.1 | ||
# The newton line search maximum step length | ||
step_size: 0.1 | ||
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# The ND voxel grid resolution | ||
resolution: 2.0 | ||
# The ND voxel grid resolution | ||
resolution: 2.0 | ||
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# The number of iterations required to calculate alignment | ||
max_iterations: 30 | ||
# The number of iterations required to calculate alignment | ||
max_iterations: 30 | ||
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# Converged param type | ||
# 0=TRANSFORM_PROBABILITY, 1=NEAREST_VOXEL_TRANSFORMATION_LIKELIHOOD | ||
converged_param_type: 1 | ||
# Number of threads used for parallel computing | ||
num_threads: 4 | ||
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# If converged_param_type is 0 | ||
# Threshold for deciding whether to trust the estimation result | ||
converged_param_transform_probability: 3.0 | ||
regularization: | ||
enable: false | ||
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# If converged_param_type is 1 | ||
# Threshold for deciding whether to trust the estimation result | ||
converged_param_nearest_voxel_transformation_likelihood: 2.3 | ||
# Regularization scale factor | ||
scale_factor: 0.01 | ||
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# The number of particles to estimate initial pose | ||
initial_estimate_particles_num: 200 | ||
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# 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 | ||
initial_pose_estimation: | ||
# The number of particles to estimate initial pose | ||
particles_num: 200 | ||
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# Tolerance of timestamp difference between current time and sensor pointcloud. [sec] | ||
lidar_topic_timeout_sec: 1.0 | ||
# 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 | ||
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# Tolerance of timestamp difference between initial_pose and sensor pointcloud. [sec] | ||
initial_pose_timeout_sec: 1.0 | ||
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# Tolerance of distance difference between two initial poses used for linear interpolation. [m] | ||
initial_pose_distance_tolerance_m: 10.0 | ||
validation: | ||
# Tolerance of timestamp difference between current time and sensor pointcloud. [sec] | ||
lidar_topic_timeout_sec: 1.0 | ||
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# The execution time which means probably NDT cannot matches scans properly. [ms] | ||
critical_upper_bound_exe_time_ms: 100.0 | ||
# Tolerance of timestamp difference between initial_pose and sensor pointcloud. [sec] | ||
initial_pose_timeout_sec: 1.0 | ||
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# Number of threads used for parallel computing | ||
num_threads: 4 | ||
# Tolerance of distance difference between two initial poses used for linear interpolation. [m] | ||
initial_pose_distance_tolerance_m: 10.0 | ||
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# 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, | ||
] | ||
# The execution time which means probably NDT cannot matches scans properly. [ms] | ||
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) | ||
use_covariance_estimation: false | ||
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# 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] | ||
score_estimation: | ||
# Converged param type | ||
# 0=TRANSFORM_PROBABILITY, 1=NEAREST_VOXEL_TRANSFORMATION_LIKELIHOOD | ||
converged_param_type: 1 | ||
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# Regularization switch | ||
regularization_enabled: false | ||
# If converged_param_type is 0 | ||
# Threshold for deciding whether to trust the estimation result | ||
converged_param_transform_probability: 3.0 | ||
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# Regularization scale factor | ||
regularization_scale_factor: 0.01 | ||
# If converged_param_type is 1 | ||
# Threshold for deciding whether to trust the estimation result | ||
converged_param_nearest_voxel_transformation_likelihood: 2.3 | ||
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# Dynamic map loading distance | ||
dynamic_map_loading_update_distance: 20.0 | ||
# Scan matching score based on no ground LiDAR scan | ||
no_ground_points: | ||
enable: false | ||
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# Dynamic map loading loading radius | ||
dynamic_map_loading_map_radius: 150.0 | ||
# If lidar_point.z - base_link.z <= this threshold , the point will be removed | ||
z_margin_for_ground_removal: 0.8 | ||
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# Radius of input LiDAR range (used for diagnostics of dynamic map loading) | ||
lidar_radius: 100.0 | ||
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# A flag for using scan matching score based on no ground LiDAR scan | ||
estimate_scores_by_no_ground_points: false | ||
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, | ||
] | ||
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# If lidar_point.z - base_link.z <= this threshold , the point will be removed | ||
z_margin_for_ground_removal: 0.8 | ||
# 2D Real-time covariance estimation with multiple searches (output_pose_covariance is the minimum value) | ||
covariance_estimation: | ||
enable: false | ||
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# 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] | ||
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dynamic_map_loading: | ||
# Dynamic map loading distance | ||
update_distance: 20.0 | ||
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# Dynamic map loading loading radius | ||
map_radius: 150.0 | ||
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# Radius of input LiDAR range (used for diagnostics of dynamic map loading) | ||
lidar_radius: 100.0 |
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