this shit ON FIRE
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69
src/target_tracking/launch/target_tracking_launch.py
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69
src/target_tracking/launch/target_tracking_launch.py
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from launch import LaunchDescription
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from launch_ros.actions import Node
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import math
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################### user configure parameters for ros2 start ###################
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# Topics/Frames
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frame_id = 'velodyne'
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topic_preprocessing_in = 'velodyne_points'
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topic_preprocessing_out = 'filtered_points'
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# Preprocessing
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x_min = 0.0
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x_max = 20.0
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z_min = -0.5
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z_max = 1.5
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tan_h_fov = math.pi / 4 # ±45°
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tan_v_fov = math.pi / 6 # ±30°
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# Clustering
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z_dim_scale = 0.2
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cluster_tolerance = 0.2
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min_cluster_size = 10
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max_cluster_size = 1000
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################### user configure parameters for ros2 end #####################
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cloud_preprocessing_params = [
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{"topic_in": topic_preprocessing_in},
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{"topic_out": topic_preprocessing_out},
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{"x_min": x_min},
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{"x_max": x_max},
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{"z_min": z_min},
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{"z_max": z_max},
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{"tan_h_fov": tan_h_fov},
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{"tan_v_fov": tan_v_fov}
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]
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cloud_clustering_params = [
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{"topic_in": topic_preprocessing_out},
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{"frame_id": frame_id},
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{"z_dim_scale": z_dim_scale},
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{"cluster_tolerance": cluster_tolerance},
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{"min_cluster_size": min_cluster_size},
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{"max_cluster_size": max_cluster_size}
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]
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def generate_launch_description():
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return LaunchDescription([
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Node(
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package='target_tracking',
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executable='cloud_preprocessing_node',
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name='cloud_preprocessing',
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parameters=cloud_preprocessing_params,
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output={
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'stdout': 'screen',
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'stderr': 'screen',
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}
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),
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Node(
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package='target_tracking',
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executable='cloud_clustering_node',
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name='cloud_clustering',
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parameters=cloud_clustering_params,
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output={
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'stdout': 'screen',
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'stderr': 'screen',
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}
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)
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])
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