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autoware_pointcloud_preprocessor package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_spheric_collision_detector autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_tensorrt_vad autoware_control_evaluator autoware_evaluation_adapter autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter autoware_diagnostic_graph_test_examples autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_camera_streampetr autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_ground_segmentation_cuda autoware_image_object_locator autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_frnet autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_predicted_path_postprocessor autoware_probabilistic_occupancy_grid_map autoware_ptv3 autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_bevformer autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_manual_lane_change_handler autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_trajectory_adapter autoware_trajectory_concatenator autoware_trajectory_modifier autoware_trajectory_optimizer autoware_trajectory_ranker autoware_trajectory_safety_filter autoware_trajectory_traffic_rule_filter autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_roundabout_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_calibration_status_classifier autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_manual_lane_change_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

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github

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Package Summary

Version	0.50.0
License	Apache License 2.0
Build type	AMENT_CMAKE
Use	RECOMMENDED

Repository Summary

Description
Checkout URI	https://github.com/autowarefoundation/autoware_universe.git
VCS Type	git
VCS Version	main
Last Updated	2026-02-25
Dev Status	UNKNOWN
Released	UNRELEASED
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

The ROS 2 autoware_pointcloud_preprocessor package

Maintainers

amc-nu
Yukihiro Saito
Kyoichi Sugahara
Dai Nguyen
Kenzo Lobos-Tsunekawa
Yihsiang Fang
Yoshi Ri
David Wong
Melike Tanrikulu
Max Schmeller
Kaan Colak

Authors

Open Perception
Julius Kammerl
William Woodall

sensing/autoware_pointcloud_preprocessor/README.md

autoware_pointcloud_preprocessor

Purpose

The autoware_pointcloud_preprocessor is a package that includes the following filters:

removing outlier points
cropping
concatenating pointclouds
correcting distortion
downsampling
densifying pointclouds

Inner-workings / Algorithms

Detail description of each filter’s algorithm is in the following links.

Filter Name	Description	Detail
concatenate_data	subscribe multiple pointclouds and concatenate them into a pointcloud	link
crop_box_filter	remove points within a given box	link
distortion_corrector	compensate pointcloud distortion caused by ego vehicle’s movement during 1 scan	link
downsample_filter	downsampling input pointcloud	link
outlier_filter	remove points caused by hardware problems, rain drops and small insects as a noise	link
passthrough_filter	remove points on the outside of a range in given field (e.g. x, y, z, intensity)	link
pointcloud_accumulator	accumulate pointclouds for a given amount of time	link
pointcloud_densifier	enhance sparse point clouds by using information from previous frames	link
vector_map_filter	remove points on the outside of lane by using vector map	link
vector_map_inside_area_filter	remove points inside of vector map area that has given type by parameter	link

Inputs / Outputs

Input

Name	Type	Description
`~/input/points`	`sensor_msgs::msg::PointCloud2`	reference points
`~/input/indices`	`pcl_msgs::msg::Indices`	reference indices

Output

Name	Type	Description
`~/output/points`	`sensor_msgs::msg::PointCloud2`	filtered points

Parameters

Node Parameters

Name	Type	Default Value	Description
`input_frame`	string	” “	input frame id
`output_frame`	string	” “	output frame id
`max_queue_size`	int	5	max queue size of input/output topics
`use_indices`	bool	false	flag to use pointcloud indices
`latched_indices`	bool	false	flag to latch pointcloud indices
`approximate_sync`	bool	false	flag to use approximate sync option

Assumptions / Known limits

autoware::pointcloud_preprocessor::Filter is implemented based on pcl_perception [1] because of this issue.

Measuring the performance

In Autoware, point cloud data from each LiDAR sensor undergoes preprocessing in the sensing pipeline before being input into the perception pipeline. The preprocessing stages are illustrated in the diagram below:

pointcloud_preprocess_pipeline.drawio.png

Each stage in the pipeline incurs a processing delay. Mostly, we’ve used ros2 topic delay /topic_name to measure the time between the message header and the current time. This approach works well for small-sized messages. However, when dealing with large point cloud messages, this method introduces an additional delay. This is primarily because accessing these large point cloud messages externally impacts the pipeline’s performance.

Our sensing/perception nodes are designed to run within composable node containers, leveraging intra-process communication. External subscriptions to these messages (like using ros2 topic delay or rviz2) impose extra delays and can even slow down the pipeline by subscribing externally. Therefore, these measurements will not be accurate.

To mitigate this issue, we’ve adopted a method where each node in the pipeline reports its pipeline latency time. This approach ensures the integrity of intra-process communication and provides a more accurate measure of delays in the pipeline.

Benchmarking The Pipeline

The nodes within the pipeline report the pipeline latency time, indicating the duration from the sensor driver’s pointcloud output to the node’s output. This data is crucial for assessing the pipeline’s health and efficiency.

When running Autoware, you can monitor the pipeline latency times for each node in the pipeline by subscribing to the following ROS 2 topics:

/sensing/lidar/LidarX/crop_box_filter_self/debug/pipeline_latency_ms
/sensing/lidar/LidarX/crop_box_filter_mirror/debug/pipeline_latency_ms
/sensing/lidar/LidarX/distortion_corrector/debug/pipeline_latency_ms
/sensing/lidar/LidarX/ring_outlier_filter/debug/pipeline_latency_ms
/sensing/lidar/concatenate_data_synchronizer/debug/sensing/lidar/LidarX/pointcloud/pipeline_latency_ms

These topics provide the pipeline latency times, giving insights into the delays at various stages of the pipeline from the sensor output of LidarX to each subsequent node.

File truncated at 100 lines see the full file

CHANGELOG

Changelog for package autoware_pointcloud_preprocessor

0.50.0 (2026-02-14)

Merge remote-tracking branch 'origin/main' into humble
test(blockage_diag): add unit tests to classes for blockage and dust detection (#12029)
- test(blockage_diag): add unit tests for blockage detection functionality
- test(blockage_diag): add unit tests for dust detection functionality
- test(blockage_diag): add unit tests for multi-frame detection aggregator
- test(blockage_diag): reduce integration tests and simplify pointcloud creation
* test(blockage_diag): optimize parameters and remove unnecessary threading in integration tests ---------Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
feat!: remove ROS 2 Galactic codes (#11905)
refactor(blockage_diag_node): separate dust detection and multi frame aggregator from blockage diag (#12024)
- refactor(blockage_diag): separate dust detection logic into its own files
- refactor(blockage_diag): separate multi-frame detection aggregator into its own files
* refactor(blockage_diag_node): remove unused includes to clean up code ---------Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>>
refactor(blockage_diag_node): extract blockage detection logic from blockage diag node (#12012)
- refactor(blockage_diag): separate dust detection diagnostic logic into evaluate_dust_detection function
- refactor(blockage_diag): extract update_diagnostics_status function for cleaner code
- refactor(blockage_diag): separate dust detection logic into DustDetector class
- refactor(blockage_diag): unified segment_into_ground_and_sky function
- style(pre-commit): autofix
- refactor(blockage_diag): add missing include directives for string and utility for cpp-lint check
- refactor(blockage_diag): extract dust detection logic into BlockageDetector class
- refactor(blockage_diag): remove unused member variables
- refactor(blockage_diag): reorder class definitions for better readability
- refactor(blockage_diag): remove unused functions definitions from header
- refactor(blockage_diag): simplify no return mask creation by removing quantization step
- refactor(blockage_diag): update diagnostics to return structured results for blockage and dust detection
- refactor(blockage_diag): update dust debug info method to use DustDetectionResult
- refactor(blockage_diag): update publish_blockage_debug_info to include blockage detection result
- refactor(blockage_diag): update debug info methods to use structured parameters
- refactor(blockage_diag): extract blockage detection logic into separate files
- refactor(blockage_diag): unify mask functions
- refactor(blockage_diag): apply clang
- refactor(blockage_diag): restore quantize_8u function to reduce diff in PR
- refactor(blockage_diag): reorder implementation to reduce diff
- fix(blockage_diag): restore lidar_depth_map publish
* style(pre-commit): autofix ---------Co-authored-by: Takahisa.Ishikawa <<takahisa.ishikawa@tier4.jp>> Co-authored-by: pre-commit-ci-lite[bot] <117423508+pre-commit-ci-lite[bot]@users.noreply.github.com>
fix(polar_voxel_outlier_filter): delete force_update() (#12006)
refactor(blockage_diag_node): extract dust detection logic from blockage diag node (#11997)
- refactor(blockage_diag): separate dust detection diagnostic logic into evaluate_dust_detection function
- refactor(blockage_diag): extract update_diagnostics_status function for cleaner code
- refactor(blockage_diag): separate dust detection logic into DustDetector class
- refactor(blockage_diag): unified segment_into_ground_and_sky function

File truncated at 100 lines see the full file

Package Dependencies

Deps	Name
	ament_cmake_auto
	autoware_cmake
	ament_lint_auto
	autoware_lint_common
	ros_testing
	autoware_internal_debug_msgs
	autoware_lanelet2_extension
	autoware_lanelet2_utils
	autoware_pcl_extensions
	autoware_point_types
	autoware_sensing_msgs
	autoware_utils
	autoware_vehicle_msgs
	cv_bridge
	diagnostic_updater
	image_transport
	managed_transform_buffer
	message_filters
	nav_msgs
	pcl_conversions
	pcl_msgs
	pcl_ros
	point_cloud_msg_wrapper
	rclcpp
	rclcpp_components
	sensor_msgs
	sophus
	std_msgs
	tf2
	tf2_eigen
	tf2_geometry_msgs
	tf2_ros
	tf2_sensor_msgs

System Dependencies

Name
cgal
libopencv-dev
libpcl-all-dev

Dependant Packages

Name	Deps
carla_sensor_kit_launch
awsim_labs_sensor_kit_launch
awsim_sensor_kit_launch
sample_sensor_kit_launch
tier4_localization_launch
tier4_perception_launch
autoware_compare_map_segmentation
autoware_ground_segmentation
autoware_ground_segmentation_cuda
autoware_image_projection_based_fusion
autoware_occupancy_grid_map_outlier_filter
autoware_probabilistic_occupancy_grid_map
autoware_cuda_pointcloud_preprocessor

Launch files

launch/approximate_downsample_filter.launch.xml
- - input_topic_name [default: /sensing/lidar/top/pointcloud_raw]
  - output_topic_name [default: /sensing/lidar/top/approximate_downsample_filter/pointcloud]
  - input_frame [default: ]
  - output_frame [default: ]
  - approximate_downsample_filter_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/approximate_downsample_filter_node.param.yaml]
launch/blockage_diag_node.launch.xml
- - input_topic_name [default: pointcloud_raw_ex]
  - output_topic_name [default: blockage_diag/pointcloud]
  - blockage_diagnostics_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/blockage_diag_node.param.yaml]
launch/concatenate_and_time_sync_node.launch.xml
- - input/twist [default: /sensing/vehicle_velocity_converter/twist_with_covariance]
  - output [default: /sensing/lidar/concatenated/pointcloud]
  - output_info [default: /sensing/lidar/concatenated/pointcloud_info]
  - param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/concatenate_and_time_sync_node.param.yaml]
launch/concatenate_pointcloud.launch.xml
- - output [default: /sensing/lidar/concatenated/pointcloud]
  - output_info [default: /sensing/lidar/concatenated/pointcloud_info]
  - param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/concatenate_pointclouds.param.yaml]
launch/crop_box_filter_node.launch.xml
- - input_topic_name [default: /sensing/lidar/top/pointcloud_raw_ex]
  - output_topic_name [default: /sensing/lidar/top/self_cropped/pointcloud_ex]
  - input_frame [default: base_link]
  - output_frame [default: base_link]
  - crop_box_filter_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/crop_box_filter_node.param.yaml]
launch/distortion_corrector_node.launch.xml
- - input/pointcloud [default: /sensing/lidar/top/mirror_cropped/pointcloud_ex]
  - input/twist [default: /sensing/vehicle_velocity_converter/twist_with_covariance]
  - input/imu [default: /sensing/imu/imu_data]
  - output/pointcloud [default: /sensing/lidar/top/rectified/pointcloud_ex]
  - param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/distortion_corrector_node.param.yaml]
launch/dual_return_outlier_filter_node.launch.xml
- - input_topic_name [default: /pointcloud]
  - output_topic_name [default: /pointcloud_filtered]
  - input_frame [default: ]
  - output_frame [default: ]
  - dual_return_outlier_filter_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/dual_return_outlier_filter_node.param.yaml]
launch/lanelet2_map_filter_node.launch.xml
- - input_vector_map [default: /map/vector_map]
  - input_pointcloud [default: detection_area/pointcloud]
  - output_pointcloud [default: vector_map_filtered/pointcloud]
  - lanelet2_map_filter_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/lanelet2_map_filter_node.param.yaml]
launch/pickup_based_voxel_grid_downsample_filter_node.launch.xml
- - input_topic_name [default: /sensing/lidar/top/pointcloud_raw]
  - output_topic_name [default: /sensing/lidar/top/pickup_based_voxel_grid_downsample_filter/pointcloud]
  - input_frame [default: ]
  - output_frame [default: ]
  - pickup_based_voxel_grid_downsample_filter_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/pickup_based_voxel_grid_downsample_filter_node.param.yaml]
launch/pointcloud_accumulator_node.launch.xml
- - input_topic_name [default: /sensing/lidar/top/pointcloud]
  - output_topic_name [default: /sensing/lidar/top/pointcloud_accumulated]
  - input_frame [default: base_link]
  - output_frame [default: base_link]
  - pointcloud_accumulator_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/pointcloud_accumulator_node.param.yaml]
launch/polar_voxel_outlier_filter_node.launch.xml
- - input_topic_name [default: /pointcloud_raw]
  - output_topic_name [default: /pointcloud_filtered]
  - input_frame [default: ]
  - output_frame [default: ]
  - polar_voxel_outlier_filter_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/polar_voxel_outlier_filter_node.param.yaml]
launch/preprocessor.launch.xml
- - input_points_raw_list [default: ['/points_raw']]
  - output_points_raw [default: /points_raw/cropbox/filtered]
  - tf_output_frame [default: base_link]
launch/radius_search_2d_outlier_filter_node.launch.xml
- - input_topic_name [default: /sensing/lidar/top/pointcloud_raw_ex]
  - output_topic_name [default: /sensing/lidar/top/pointcloud_filtered]
  - input_frame [default: base_link]
  - output_frame [default: base_link]
  - radius_search_2d_outlier_filter_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/radius_search_2d_outlier_filter_node.param.yaml]
launch/random_downsample_filter_node.launch.xml
- - input_topic_name [default: /sensing/lidar/top/pointcloud_raw]
  - output_topic_name [default: /sensing/lidar/top/random_downsample_filter/pointcloud]
  - input_frame [default: ]
  - output_frame [default: ]
  - random_downsample_filter_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/random_downsample_filter_node.param.yaml]
launch/ring_outlier_filter_node.launch.xml
- - input_topic_name [default: /sensing/lidar/top/pointcloud_raw_ex]
  - output_topic_name [default: /sensing/lidar/top/pointcloud_ring_filtered]
  - input_frame [default: ]
  - output_frame [default: ]
  - ring_outlier_filter_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/ring_outlier_filter_node.param.yaml]
launch/ring_passthrough_filter.launch.xml
- - input_topic_name [default: /sensing/lidar/top/pointcloud_raw]
  - output_topic_name [default: /sensing/lidar/top/ring_passthrough_filtered/pointcloud]
  - input_frame [default: ]
  - output_frame [default: base_link]
  - passthrough_filter_uint16_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/passthrough_filter_uint16_node.param.yaml]
launch/time_synchronizer_node.launch.xml
- - input/twist [default: /sensing/vehicle_velocity_converter/twist_with_covariance]
  - param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/time_synchronizer_node.param.yaml]
launch/vector_map_inside_area_filter_node.launch.xml
- - input_vector_map [default: /map/vector_map]
  - input_pointcloud [default: compare_map_filtered/pointcloud]
  - output_pointcloud [default: vector_map_inside_area_filtered/pointcloud]
  - vector_map_inside_area_filter_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/vector_map_inside_area_filter_node.param.yaml]
launch/voxel_grid_downsample_filter_node.launch.xml
- - input_topic_name [default: /sensing/lidar/top/pointcloud_raw]
  - output_topic_name [default: /sensing/lidar/top/voxel_grid_downsample_filter/pointcloud]
  - input_frame [default: base_link]
  - output_frame [default: base_link]
  - voxel_grid_downsample_filter_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/voxel_grid_downsample_filter_node.param.yaml]
launch/voxel_grid_outlier_filter_node.launch.xml
- - input_topic_name [default: /pointcloud_raw]
  - output_topic_name [default: /pointcloud_filtered]
  - input_frame [default: ]
  - output_frame [default: ]
  - voxel_grid_outlier_filter_param_file [default: $(find-pkg-share autoware_pointcloud_preprocessor)/config/voxel_grid_outlier_filter_node.param.yaml]

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