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autoware_lidar_centerpoint 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_lidar_centerpoint package

Additional Links

No additional links.

Maintainers

Kenzo Lobos-Tsunekawa
Amadeusz Szymko
Kotaro Uetake
Masato Saeki
Taekjin Lee
Kok Seang Tan

Authors

No additional authors.

perception/autoware_lidar_centerpoint/README.md

autoware_lidar_centerpoint

Purpose

autoware_lidar_centerpoint is a package for detecting dynamic 3D objects.

Inner-workings / Algorithms

In this implementation, CenterPoint [1] uses a PointPillars-based [2] network to inference with TensorRT.

We trained the models using https://github.com/open-mmlab/mmdetection3d.

Inputs / Outputs

Input

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

Output

Name	Type	Description
`~/output/objects`	`autoware_perception_msgs::msg::DetectedObjects`	detected objects
`debug/cyclic_time_ms`	`autoware_internal_debug_msgs::msg::Float64Stamped`	cyclic time (msg)
`debug/processing_time_ms`	`autoware_internal_debug_msgs::msg::Float64Stamped`	processing time (ms)

Parameters

ML Model Parameters

Note that these parameters are associated with ONNX file, predefined during the training phase. Be careful to change ONNX file as well when changing this parameter. Also, whenever you update the ONNX file, do NOT forget to check these values.

Name	Type	Default Value	Description
`model_params.class_names`	list[string]	[“CAR”, “TRUCK”, “BUS”, “BICYCLE”, “PEDESTRIAN”]	list of class names for model outputs
`model_params.point_feature_size`	int	`4`	number of features per point in the point cloud
`model_params.max_voxel_size`	int	`40000`	maximum number of voxels
`model_params.point_cloud_range`	list[double]	[-76.8, -76.8, -4.0, 76.8, 76.8, 6.0]	detection range [min_x, min_y, min_z, max_x, max_y, max_z] [m]
`model_params.voxel_size`	list[double]	[0.32, 0.32, 10.0]	size of each voxel [x, y, z] [m]
`model_params.downsample_factor`	int	`1`	downsample factor for coordinates
`model_params.encoder_in_feature_size`	int	`9`	number of input features to the encoder
`model_params.has_variance`	bool	`false`	true if the model outputs pose variance as well as pose for each bbox
`model_params.has_twist`	bool	`false`	true if the model outputs velocity as well as pose for each bbox

Core Parameters

Name	Type	Default Value	Description
`encoder_onnx_path`	string	`""`	path to VoxelFeatureEncoder ONNX file
`encoder_engine_path`	string	`""`	path to VoxelFeatureEncoder TensorRT Engine file
`head_onnx_path`	string	`""`	path to DetectionHead ONNX file
`head_engine_path`	string	`""`	path to DetectionHead TensorRT Engine file
`build_only`	bool	`false`	shutdown the node after TensorRT engine file is built
`trt_precision`	string	`fp16`	TensorRT inference precision: `fp32` or `fp16`
`post_process_params.score_thresholds`	list[double]	[0.35, 0.35, 0.35, 0.35, 0.35]	detected objects with score less than their label threshold are ignored.
`post_process_params.yaw_norm_thresholds`	list[double]	[0.3, 0.3, 0.3, 0.3, 0.0]	An array of distance threshold values of norm of yaw [rad].
`post_process_params.iou_nms_search_distance_2d`	double	-	If two objects are farther than the value, NMS isn’t applied.
`post_process_params.iou_nms_threshold`	double	-	IoU threshold for the IoU-based Non Maximum Suppression
`post_process_params.has_twist`	boolean	false	Indicates whether the model outputs twist value.
`densification_params.world_frame_id`	string	`map`	the world frame id to fuse multi-frame pointcloud
`densification_params.num_past_frames`	int	`1`	the number of past frames to fuse with the current frame

The `build_only` option

The autoware_lidar_centerpoint node has build_only option to build the TensorRT engine file from the ONNX file. Although it is preferred to move all the ROS parameters in .param.yaml file in Autoware Universe, the build_only option is not moved to the .param.yaml file for now, because it may be used as a flag to execute the build as a pre-task. You can execute with the following command:

ros2 launch autoware_lidar_centerpoint lidar_centerpoint.launch.xml model_name:=centerpoint_tiny model_path:=/home/autoware/autoware_data/lidar_centerpoint model_param_path:=$(ros2 pkg prefix autoware_lidar_centerpoint --share)/config/centerpoint_tiny.param.yaml build_only:=true

Assumptions / Known limits

The object.existence_probability is stored the value of classification confidence of a DNN, not probability.

Trained Models

You can download the onnx format of trained models by clicking on the links below.

Centerpoint: pts_voxel_encoder_centerpoint.onnx, pts_backbone_neck_head_centerpoint.onnx
Centerpoint tiny: pts_voxel_encoder_centerpoint_tiny.onnx, pts_backbone_neck_head_centerpoint_tiny.onnx

Centerpoint was trained in nuScenes (~28k lidar frames) [8] and TIER IV’s internal database (~11k lidar frames) for 60 epochs. Centerpoint tiny was trained in Argoverse 2 (~110k lidar frames) [9] and TIER IV’s internal database (~11k lidar frames) for 20 epochs.

Training CenterPoint Model and Deploying to the Autoware

Overview

This guide provides instructions on training a CenterPoint model using the mmdetection3d repository and seamlessly deploying it within Autoware.

Installation

Install prerequisites

Step 1. Download and install Miniconda from the official website.

File truncated at 100 lines see the full file

CHANGELOG

Changelog for package autoware_lidar_centerpoint

0.47.0 (2025-08-11)

feat(autoware_lidar_centerpoint): add class-wise confidence thresholds to CenterPoint (#10881)
- Add PreprocessCuda to CenterPoint
- style(pre-commit): autofix
- style(pre-commit): autofix
- Add intensity preprocessing
- style(pre-commit): autofix
- Fix config_.point_feature_size_ typo
- style(pre-commit): autofix
- Fix point typo
- style(pre-commit): autofix
- Change score_threshold to score_thresholds
- Use <autoware/cuda_utils/cuda_utils.hpp> for clear_async
- Rename pre_ptr_ to pre_proc_ptr_
- Remove unused getCacheSize() and getIdx
- Use template in generateVoxels_random_kernel instead
- style(pre-commit): autofix
- Remove references in generateVoxels_random_kernel
- Remove references in generateVoxels_random_kernel
- style(pre-commit): autofix
- Remove generateIntensityFeatures_kernel and add the case of 11 to ENCODER_IN_FEATURE_SIZE for generateFeatures_kernel
- style(pre-commit): autofix
- Add class-wise confidence thresholds to CenterPoint
- style(pre-commit): autofix
- Remov empty line changes
- Update score_threshold to score_thresholds in REAMME
- style(pre-commit): autofix
- Change score_thresholds from pass by value to pass by reference
- style(pre-commit): autofix
- Add information about class names in scehema
- Change vector<double> to vector<float>
- Remove thrust and add stream_ to PostProcessCUDA
- style(pre-commit): autofix
- Fix incorrect initialization of score_thresholds_ vector
- Fix postprocess CudaMemCpy error
- Fix postprocess score_thresholds_d_ptr_ typing error
- Fix score_thresholds typing in node.cpp
- Static casting params.score_thresholds vector
- style(pre-commit): autofix
- Update perception/autoware_lidar_centerpoint/src/node.cpp
- Update perception/autoware_lidar_centerpoint/include/autoware/lidar_centerpoint/centerpoint_config.hpp
- Update centerpoint_config.hpp
- Update node.cpp
- Update score_thresholds_ to double since ros2 supports only double instead of float
- style(pre-commit): autofix
- Fix cuda memory and revert double score_thresholds_ to float score_thresholds_
* style(pre-commit): autofix ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Taekjin LEE <<technolojin@gmail.com>>
feat(autoware_lidar_centerpoint): add Intensity support to CenterPoint (#10854)
- Add PreprocessCuda to CenterPoint
- style(pre-commit): autofix
- style(pre-commit): autofix
- Add intensity preprocessing
- style(pre-commit): autofix
- Fix config_.point_feature_size_ typo
- style(pre-commit): autofix
- Fix point typo
- style(pre-commit): autofix
- Use <autoware/cuda_utils/cuda_utils.hpp> for clear_async
- Rename pre_ptr_ to pre_proc_ptr_
- Remove unused getCacheSize() and getIdx
- Use template in generateVoxels_random_kernel instead
- style(pre-commit): autofix
- Remove references in generateVoxels_random_kernel
- Remove references in generateVoxels_random_kernel
- style(pre-commit): autofix
- Remove generateIntensityFeatures_kernel and add the case of 11 to ENCODER_IN_FEATURE_SIZE for generateFeatures_kernel
- style(pre-commit): autofix
* Remov empty line changes ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Contributors: Kok Seang Tan

0.46.0 (2025-06-20)

Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base
chore(perception): delete maintainer name (#10816)

File truncated at 100 lines see the full file

Package Dependencies

Deps	Name
	ament_cmake_auto
	ament_cmake_python
	autoware_cmake
	ament_lint_auto
	autoware_lint_common
	autoware_cuda_dependency_meta
	autoware_cuda_utils
	autoware_object_recognition_utils
	autoware_perception_msgs
	autoware_tensorrt_common
	autoware_utils
	cuda_blackboard
	pcl_ros
	rclcpp
	rclcpp_components
	tf2_eigen
	tf2_geometry_msgs
	tf2_sensor_msgs

System Dependencies

No direct system dependencies.

Dependant Packages

Name	Deps
tier4_perception_launch
autoware_image_projection_based_fusion

Launch files

launch/lidar_centerpoint.launch.xml
- - input/pointcloud [default: /sensing/lidar/pointcloud]
  - output/objects [default: objects]
  - data_path [default: $(env HOME)/autoware_data]
  - node_name [default: lidar_centerpoint]
  - model_name [default: centerpoint_tiny]
  - model_path [default: $(var data_path)/$(var node_name)]
  - model_param_path [default: $(find-pkg-share autoware_lidar_centerpoint)/config/$(var model_name).param.yaml]
  - ml_package_param_path [default: $(var model_path)/$(var model_name)_ml_package.param.yaml]
  - class_remapper_param_path [default: $(var model_path)/detection_class_remapper.param.yaml]
  - common_param_path [default: $(find-pkg-share autoware_lidar_centerpoint)/config/centerpoint_common.param.yaml]
  - build_only [default: false]
  - use_pointcloud_container [default: false]
  - pointcloud_container_name [default: pointcloud_container]

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

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