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autoware_euclidean_cluster package from autoware_universe repo

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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_rtc_interface autoware_behavior_velocity_run_out_module 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_cuda_pointcloud_preprocessor 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Package Summary

Tags	No category tags.
Version	0.47.0
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	2025-08-16
Dev Status	UNKNOWN
Released	UNRELEASED
Tags	planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing	Help Wanted (-) Good First Issues (-) Pull Requests to Review (-)

Package Description

The autoware_euclidean_cluster package

Additional Links

No additional links.

Maintainers

Yukihiro Saito
Dai Nguyen
Lei Gu
Taekjin Lee

Authors

No additional authors.

perception/autoware_euclidean_cluster/README.md

autoware_euclidean_cluster

Purpose

autoware_euclidean_cluster is a package for clustering points into smaller parts to classify objects.

This package has two clustering methods: euclidean_cluster and voxel_grid_based_euclidean_cluster.

Inner-workings / Algorithms

euclidean_cluster

pcl::EuclideanClusterExtraction is applied to points. See official document for details.

voxel_grid_based_euclidean_cluster

A centroid in each voxel is calculated by pcl::VoxelGrid.
The centroids are clustered by pcl::EuclideanClusterExtraction.
The input points are clustered based on the clustered centroids.

Inputs / Outputs

Input

Name	Type	Description
`input`	`sensor_msgs::msg::PointCloud2`	input pointcloud

Output

Name	Type	Description
`output`	`tier4_perception_msgs::msg::DetectedObjectsWithFeature`	cluster pointcloud
`debug/clusters`	`sensor_msgs::msg::PointCloud2`	colored cluster pointcloud for visualization

Parameters

Core Parameters

euclidean_cluster

Name	Type	Description
`use_height`	bool	use point.z for clustering
`min_cluster_size`	int	the minimum number of points that a cluster needs to contain in order to be considered valid
`max_cluster_size`	int	the maximum number of points that a cluster needs to contain in order to be considered valid
`tolerance`	float	the spatial cluster tolerance as a measure in the L2 Euclidean space

voxel_grid_based_euclidean_cluster

Name	Type	Description
`use_height`	bool	use point.z for clustering
`min_cluster_size`	int	the minimum number of voxels that a cluster needs to contain in order to be considered valid
`max_cluster_size`	int	the maximum number of voxels that a cluster needs to contain in order to be considered valid
`tolerance`	float	the spatial cluster tolerance as a measure in the L2 Euclidean space
`voxel_leaf_size`	float	the voxel leaf size of x and y
`min_points_number_per_voxel`	int	the minimum number of points for a voxel
`min_voxel_cluster_size_for_filtering`	int	The minimum voxel cluster size for a cluster to be checked for being a large cluster.
`max_points_per_voxel_in_large_cluster`	int	The maximum points per voxel allowed in large clusters (used for filtering dense clusters).
`max_voxel_cluster_for_output`	int	The maximum number of voxel clusters to output. If the voxels exceeds this value, the cluster will be skipped.

Assumptions / Known limits

(Optional) Error detection and handling

(Optional) Performance characterization

(Optional) References/External links

<!– Write links you referred to when you implemented.

Example: [1] {link_to_a_thesis} [2] {link_to_an_issue}

File truncated at 100 lines see the full file

CHANGELOG

Changelog for package autoware_euclidean_cluster

0.47.0 (2025-08-11)

0.46.0 (2025-06-20)

Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base
feat(autoware_pointcloud_preprocessor): add diagnostic message (#10579)
- feat: add diag msg
- chore: fix code
- chore: remove outlier count in ring
- chore: move format timestamp to utility
- chore: add paramter to schema
- chore: add parameter for cluster
- chore: clean code
- chore: fix schema
- chore: move diagnostic updater to filter base class
- chore: fix schema
- chore: fix spell error
- chore: set up diagnostic updater
- refactor: utilize autoware_utils diagnostic message
- chore: add publish
- chore: add detail message
- chore: const for time difference
- refactor: structure diagnostics to class
- chore: const reference
- chore: clean logic
- chore: modify function name
- chore: update parameter
- chore: move evaluate status into diagnostic
- chore: fix description for concatenated pointcloud
- chore: timestamp mismatch threshold
- chore: fix diagnostic key
* chore: change function naming ---------
feat(autoware_euclidean_cluster): enhance VoxelGridBasedEuclideanCluster with Large Cluster Filtering Parameters (#10618)

* Squashed commit of the following: commit cf3035909ccad94003b2b06f8608b6cb887b221a Author: lei.gu <<lei.gu@tier4.jp>> Date: Tue May 13 11:34:32 2025 +0900 debugging impl removed commit 17ee5fc61053e1ff816294a962d9f61dc73cd164 Author: lei.gu <<lei.gu@tier4.jp>> Date: Tue May 13 11:24:04 2025 +0900 parameters reading finished commit 6731b5150344515fce11bf5c0128a20145a0b6a8 Author: lei.gu <<lei.gu@tier4.jp>> Date: Tue May 13 09:50:47 2025 +0900 euclidean cluster filter commit 4a65dafec7728209dc4015c513920215d259ddae Author: lei.gu <<lei.gu@tier4.jp>> Date: Fri May 9 15:46:07 2025 +0900 Squashed commit of the following: commit 699e657c3997e0c3457d9c1f5fffe1081c4433cc Merge: 4833afd811 e876ece2f8 Author: lei.gu <<lei.gu@tier4.jp>> Date: Fri May 9 11:14:48 2025 +0900 Merge branch 'main' into feat/autoware_perception_rviz_plugin/detected_objects_with_feature_display commit 4833afd8114364625a4a9a82b237e72a09c737be Author: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri May 9 02:13:09 2025 +0000 style(pre-commit): autofix commit d7bf97d85c1c97300adf52b7ace62a7c08b78402 Author: lei.gu <<lei.gu@tier4.jp>> Date: Fri May 9 10:53:33 2025 +0900 fix all problems of rviz commit 91ec2882a505df6996d49a2395f977eae1841314 Author: lei.gu <<lei.gu@tier4.jp>> Date: Thu May 8 19:22:56 2025 +0900 format fix commit fa1e680ab138253831398c51c415dd3861ea298b Author: lei.gu <<lei.gu@tier4.jp>> Date: Thu May 8 17:56:16 2025 +0900 helper to better structure commit 2e4ba008e8c3351fc12f33b79f2fe41c492b1f3c Author: lei.gu <<lei.gu@tier4.jp>> Date: Thu May 8 16:26:16 2025 +0900 colorbar visualization optimized commit 25e4b9f4131cf38ce89c9b8b28dee0ed6562a4c3 Author: lei.gu <<lei.gu@tier4.jp>> Date: Thu May 8 15:42:48 2025 +0900 basic functions all implemented commit 3e3db86a1f3ff266cb00b8b83fa57231ee8e2fb8 Author: lei.gu <<lei.gu@tier4.jp>> Date: Thu May 8 10:31:02 2025 +0900 colorbar commit a6be3ce4a2a3fc48b54ba798af7875d6b071d88b Author: lei.gu <<lei.gu@tier4.jp>> Date: Wed May 7 18:05:45 2025 +0900 colormap fully implemented commit 46762b344541580d3411f61ea78828e5f35d9cfb Author: lei.gu <<lei.gu@tier4.jp>> Date: Wed May 7 17:49:07 2025 +0900 colormap implemented commit e3024f1d2865ca76c0b8e338fa5c2d6bd282dd22 Author: lei.gu <<lei.gu@tier4.jp>> Date: Thu Apr 17 10:30:24 2025 +0900 feat(euclidean_cluster): add markers for clusters remove filter profiling rviz detected_objects_with_feature detected objects stage all commits Revert non-visualization changes to state of 43480ef7 commit daef21efb35bc0c4dc2fe9009906199d2b3cf9b1 Author: lei.gu <<lei.gu@tier4.jp>> Date: Fri May 9 15:36:16 2025 +0900 colorbar
- style(pre-commit): autofix
- corresponding part of VoxelGridBasedEuclideanCluster used in detection by tracker
- style(pre-commit): autofix
- cluster point number diag removed
- add max_num_points_per_cluster
- euclidean cluster diag impl restored
- add comments for the parameters

File truncated at 100 lines see the full file

Package Dependencies

Deps	Name
	ament_cmake_auto
	autoware_cmake
	ament_cmake_gtest
	ament_lint_auto
	autoware_lint_common
	autoware_perception_msgs
	autoware_point_types
	autoware_utils
	geometry_msgs
	pcl_conversions
	rclcpp
	rclcpp_components
	sensor_msgs
	tier4_perception_msgs

System Dependencies

Name
libpcl-all-dev

Dependant Packages

Name	Deps
tier4_perception_launch
autoware_detection_by_tracker
autoware_image_projection_based_fusion

Launch files

launch/euclidean_cluster.launch.xml
- - input_pointcloud [default: /perception/obstacle_segmentation/pointcloud]
  - input_map [default: /map/pointcloud_map]
  - output_clusters [default: clusters]
  - use_low_height_cropbox [default: false]
  - euclidean_param_path [default: $(find-pkg-share autoware_euclidean_cluster)/config/euclidean_cluster.param.yaml]
  - use_pointcloud_container [default: false]
  - pointcloud_container_name [default: pointcloud_container]
launch/voxel_grid_based_euclidean_cluster.launch.xml
- - input_pointcloud [default: /perception/obstacle_segmentation/pointcloud]
  - input_map [default: /map/pointcloud_map]
  - output_clusters [default: clusters]
  - use_low_height_cropbox [default: false]
  - voxel_grid_based_euclidean_param_path [default: $(find-pkg-share autoware_euclidean_cluster)/config/voxel_grid_based_euclidean_cluster.param.yaml]
  - use_pointcloud_container [default: false]
  - pointcloud_container_name [default: pointcloud_container]

Messages

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Services

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Plugins

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