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
Additional Links
Maintainers
- Amadeusz Szymko
- Kenzo Lobos-Tsunekawa
- Kotaro Uetake
- Masato Saeki
- Taekjin Lee
- Kok Seang Tan
Authors
autoware_lidar_transfusion
Purpose
The autoware_lidar_transfusion
package is used for 3D object detection based on lidar data (x, y, z, intensity).
Inner-workings / Algorithms
The implementation bases on TransFusion [1] work. It uses TensorRT library for data process and network inference.
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 (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
TransFusion node
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion.schema.dummy.json”) }}
TransFusion model
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion_ml_package.schema.json”) }}
Detection class remapper
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/detection_class_remapper.schema.json”) }}
The build_only
option
The autoware_lidar_transfusion
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_transfusion lidar_transfusion.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_lidar_transfusion
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_lidar_transfusion lidar_transfusion.launch.xml log_level:=debug
Assumptions / Known limits
This library operates on raw cloud data (bytes). It is assumed that the input pointcloud message has following format:
[
sensor_msgs.msg.PointField(name='x', offset=0, datatype=7, count=1),
sensor_msgs.msg.PointField(name='y', offset=4, datatype=7, count=1),
sensor_msgs.msg.PointField(name='z', offset=8, datatype=7, count=1),
sensor_msgs.msg.PointField(name='intensity', offset=12, datatype=2, count=1)
]
This input may consist of other fields as well - shown format is required minimum. For debug purposes, you can validate your pointcloud topic using simple command:
ros2 topic echo <input_topic> --field fields
Trained Models
You can download the onnx format of trained models by clicking on the links below.
- TransFusion: transfusion.onnx
The model was trained in TIER IV’s internal database (~11k lidar frames) for 50 epochs.
Changelog
(Optional) Error detection and handling
File truncated at 100 lines see the full file
Changelog for package autoware_lidar_transfusion
0.47.0 (2025-08-11)
0.46.0 (2025-06-20)
- Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base
- chore(perception): delete maintainer name (#10816)
- Contributors: Satoshi Tanaka, TaikiYamada4
0.45.0 (2025-05-22)
-
Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
-
chore: perception code owner update (#10645)
- chore: update maintainers in multiple perception packages
* Revert "chore: update maintainers in multiple perception packages" This reverts commit f2838c33d6cd82bd032039e2a12b9cb8ba6eb584.
- chore: update maintainers in multiple perception packages
* chore: add Kok Seang Tan as maintainer in multiple perception packages ---------
-
feat(autoware_lidar_transfusion): added target architectures for transfusion (#10615)
- chore: added target architectures for transfusion
- chore: removed unnecessary flag
* chore: mistook the architecture of edge devices ---------
-
feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion (#10513)
- feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion
* fix(autoware_lidar_transfusion): clang-tidy errors ---------Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>>
-
Contributors: Amadeusz Szymko, Kenzo Lobos Tsunekawa, Taekjin LEE, TaikiYamada4
0.44.2 (2025-06-10)
0.44.1 (2025-05-01)
0.44.0 (2025-04-18)
-
Merge remote-tracking branch 'origin/main' into humble
-
feat(lidar_transfusion): add diagnostics for processing time (#10398)
- add processing time diagnostics
- style(pre-commit): autofix
* remove comment ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
-
Contributors: Masaki Baba, Ryohsuke Mitsudome
0.43.0 (2025-03-21)
- Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
- chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
- refactor: add autoware_cuda_dependency_meta (#10073)
- Contributors: Esteve Fernandez, Hayato Mizushima, Yutaka Kondo
0.42.0 (2025-03-03)
- Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
- feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
- Contributors: Fumiya Watanabe, 心刚
0.41.2 (2025-02-19)
- chore: bump version to 0.41.1 (#10088)
- Contributors: Ryohsuke Mitsudome
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
Name | Deps |
---|---|
tier4_perception_launch |
Launch files
- launch/lidar_transfusion.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- output/objects [default: objects]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: transfusion]
- model_path [default: $(var data_path)/lidar_transfusion]
- model_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/$(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: $(find-pkg-share autoware_lidar_transfusion)/config/detection_class_remapper.param.yaml]
- common_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/transfusion_common.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_lidar_transfusion at Robotics Stack Exchange
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
Additional Links
Maintainers
- Amadeusz Szymko
- Kenzo Lobos-Tsunekawa
- Kotaro Uetake
- Masato Saeki
- Taekjin Lee
- Kok Seang Tan
Authors
autoware_lidar_transfusion
Purpose
The autoware_lidar_transfusion
package is used for 3D object detection based on lidar data (x, y, z, intensity).
Inner-workings / Algorithms
The implementation bases on TransFusion [1] work. It uses TensorRT library for data process and network inference.
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 (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
TransFusion node
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion.schema.dummy.json”) }}
TransFusion model
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion_ml_package.schema.json”) }}
Detection class remapper
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/detection_class_remapper.schema.json”) }}
The build_only
option
The autoware_lidar_transfusion
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_transfusion lidar_transfusion.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_lidar_transfusion
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_lidar_transfusion lidar_transfusion.launch.xml log_level:=debug
Assumptions / Known limits
This library operates on raw cloud data (bytes). It is assumed that the input pointcloud message has following format:
[
sensor_msgs.msg.PointField(name='x', offset=0, datatype=7, count=1),
sensor_msgs.msg.PointField(name='y', offset=4, datatype=7, count=1),
sensor_msgs.msg.PointField(name='z', offset=8, datatype=7, count=1),
sensor_msgs.msg.PointField(name='intensity', offset=12, datatype=2, count=1)
]
This input may consist of other fields as well - shown format is required minimum. For debug purposes, you can validate your pointcloud topic using simple command:
ros2 topic echo <input_topic> --field fields
Trained Models
You can download the onnx format of trained models by clicking on the links below.
- TransFusion: transfusion.onnx
The model was trained in TIER IV’s internal database (~11k lidar frames) for 50 epochs.
Changelog
(Optional) Error detection and handling
File truncated at 100 lines see the full file
Changelog for package autoware_lidar_transfusion
0.47.0 (2025-08-11)
0.46.0 (2025-06-20)
- Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base
- chore(perception): delete maintainer name (#10816)
- Contributors: Satoshi Tanaka, TaikiYamada4
0.45.0 (2025-05-22)
-
Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
-
chore: perception code owner update (#10645)
- chore: update maintainers in multiple perception packages
* Revert "chore: update maintainers in multiple perception packages" This reverts commit f2838c33d6cd82bd032039e2a12b9cb8ba6eb584.
- chore: update maintainers in multiple perception packages
* chore: add Kok Seang Tan as maintainer in multiple perception packages ---------
-
feat(autoware_lidar_transfusion): added target architectures for transfusion (#10615)
- chore: added target architectures for transfusion
- chore: removed unnecessary flag
* chore: mistook the architecture of edge devices ---------
-
feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion (#10513)
- feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion
* fix(autoware_lidar_transfusion): clang-tidy errors ---------Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>>
-
Contributors: Amadeusz Szymko, Kenzo Lobos Tsunekawa, Taekjin LEE, TaikiYamada4
0.44.2 (2025-06-10)
0.44.1 (2025-05-01)
0.44.0 (2025-04-18)
-
Merge remote-tracking branch 'origin/main' into humble
-
feat(lidar_transfusion): add diagnostics for processing time (#10398)
- add processing time diagnostics
- style(pre-commit): autofix
* remove comment ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
-
Contributors: Masaki Baba, Ryohsuke Mitsudome
0.43.0 (2025-03-21)
- Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
- chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
- refactor: add autoware_cuda_dependency_meta (#10073)
- Contributors: Esteve Fernandez, Hayato Mizushima, Yutaka Kondo
0.42.0 (2025-03-03)
- Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
- feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
- Contributors: Fumiya Watanabe, 心刚
0.41.2 (2025-02-19)
- chore: bump version to 0.41.1 (#10088)
- Contributors: Ryohsuke Mitsudome
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
Name | Deps |
---|---|
tier4_perception_launch |
Launch files
- launch/lidar_transfusion.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- output/objects [default: objects]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: transfusion]
- model_path [default: $(var data_path)/lidar_transfusion]
- model_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/$(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: $(find-pkg-share autoware_lidar_transfusion)/config/detection_class_remapper.param.yaml]
- common_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/transfusion_common.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_lidar_transfusion at Robotics Stack Exchange
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
Additional Links
Maintainers
- Amadeusz Szymko
- Kenzo Lobos-Tsunekawa
- Kotaro Uetake
- Masato Saeki
- Taekjin Lee
- Kok Seang Tan
Authors
autoware_lidar_transfusion
Purpose
The autoware_lidar_transfusion
package is used for 3D object detection based on lidar data (x, y, z, intensity).
Inner-workings / Algorithms
The implementation bases on TransFusion [1] work. It uses TensorRT library for data process and network inference.
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 (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
TransFusion node
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion.schema.dummy.json”) }}
TransFusion model
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion_ml_package.schema.json”) }}
Detection class remapper
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/detection_class_remapper.schema.json”) }}
The build_only
option
The autoware_lidar_transfusion
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_transfusion lidar_transfusion.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_lidar_transfusion
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_lidar_transfusion lidar_transfusion.launch.xml log_level:=debug
Assumptions / Known limits
This library operates on raw cloud data (bytes). It is assumed that the input pointcloud message has following format:
[
sensor_msgs.msg.PointField(name='x', offset=0, datatype=7, count=1),
sensor_msgs.msg.PointField(name='y', offset=4, datatype=7, count=1),
sensor_msgs.msg.PointField(name='z', offset=8, datatype=7, count=1),
sensor_msgs.msg.PointField(name='intensity', offset=12, datatype=2, count=1)
]
This input may consist of other fields as well - shown format is required minimum. For debug purposes, you can validate your pointcloud topic using simple command:
ros2 topic echo <input_topic> --field fields
Trained Models
You can download the onnx format of trained models by clicking on the links below.
- TransFusion: transfusion.onnx
The model was trained in TIER IV’s internal database (~11k lidar frames) for 50 epochs.
Changelog
(Optional) Error detection and handling
File truncated at 100 lines see the full file
Changelog for package autoware_lidar_transfusion
0.47.0 (2025-08-11)
0.46.0 (2025-06-20)
- Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base
- chore(perception): delete maintainer name (#10816)
- Contributors: Satoshi Tanaka, TaikiYamada4
0.45.0 (2025-05-22)
-
Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
-
chore: perception code owner update (#10645)
- chore: update maintainers in multiple perception packages
* Revert "chore: update maintainers in multiple perception packages" This reverts commit f2838c33d6cd82bd032039e2a12b9cb8ba6eb584.
- chore: update maintainers in multiple perception packages
* chore: add Kok Seang Tan as maintainer in multiple perception packages ---------
-
feat(autoware_lidar_transfusion): added target architectures for transfusion (#10615)
- chore: added target architectures for transfusion
- chore: removed unnecessary flag
* chore: mistook the architecture of edge devices ---------
-
feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion (#10513)
- feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion
* fix(autoware_lidar_transfusion): clang-tidy errors ---------Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>>
-
Contributors: Amadeusz Szymko, Kenzo Lobos Tsunekawa, Taekjin LEE, TaikiYamada4
0.44.2 (2025-06-10)
0.44.1 (2025-05-01)
0.44.0 (2025-04-18)
-
Merge remote-tracking branch 'origin/main' into humble
-
feat(lidar_transfusion): add diagnostics for processing time (#10398)
- add processing time diagnostics
- style(pre-commit): autofix
* remove comment ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
-
Contributors: Masaki Baba, Ryohsuke Mitsudome
0.43.0 (2025-03-21)
- Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
- chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
- refactor: add autoware_cuda_dependency_meta (#10073)
- Contributors: Esteve Fernandez, Hayato Mizushima, Yutaka Kondo
0.42.0 (2025-03-03)
- Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
- feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
- Contributors: Fumiya Watanabe, 心刚
0.41.2 (2025-02-19)
- chore: bump version to 0.41.1 (#10088)
- Contributors: Ryohsuke Mitsudome
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
Name | Deps |
---|---|
tier4_perception_launch |
Launch files
- launch/lidar_transfusion.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- output/objects [default: objects]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: transfusion]
- model_path [default: $(var data_path)/lidar_transfusion]
- model_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/$(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: $(find-pkg-share autoware_lidar_transfusion)/config/detection_class_remapper.param.yaml]
- common_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/transfusion_common.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_lidar_transfusion at Robotics Stack Exchange
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
Additional Links
Maintainers
- Amadeusz Szymko
- Kenzo Lobos-Tsunekawa
- Kotaro Uetake
- Masato Saeki
- Taekjin Lee
- Kok Seang Tan
Authors
autoware_lidar_transfusion
Purpose
The autoware_lidar_transfusion
package is used for 3D object detection based on lidar data (x, y, z, intensity).
Inner-workings / Algorithms
The implementation bases on TransFusion [1] work. It uses TensorRT library for data process and network inference.
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 (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
TransFusion node
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion.schema.dummy.json”) }}
TransFusion model
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion_ml_package.schema.json”) }}
Detection class remapper
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/detection_class_remapper.schema.json”) }}
The build_only
option
The autoware_lidar_transfusion
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_transfusion lidar_transfusion.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_lidar_transfusion
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_lidar_transfusion lidar_transfusion.launch.xml log_level:=debug
Assumptions / Known limits
This library operates on raw cloud data (bytes). It is assumed that the input pointcloud message has following format:
[
sensor_msgs.msg.PointField(name='x', offset=0, datatype=7, count=1),
sensor_msgs.msg.PointField(name='y', offset=4, datatype=7, count=1),
sensor_msgs.msg.PointField(name='z', offset=8, datatype=7, count=1),
sensor_msgs.msg.PointField(name='intensity', offset=12, datatype=2, count=1)
]
This input may consist of other fields as well - shown format is required minimum. For debug purposes, you can validate your pointcloud topic using simple command:
ros2 topic echo <input_topic> --field fields
Trained Models
You can download the onnx format of trained models by clicking on the links below.
- TransFusion: transfusion.onnx
The model was trained in TIER IV’s internal database (~11k lidar frames) for 50 epochs.
Changelog
(Optional) Error detection and handling
File truncated at 100 lines see the full file
Changelog for package autoware_lidar_transfusion
0.47.0 (2025-08-11)
0.46.0 (2025-06-20)
- Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base
- chore(perception): delete maintainer name (#10816)
- Contributors: Satoshi Tanaka, TaikiYamada4
0.45.0 (2025-05-22)
-
Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
-
chore: perception code owner update (#10645)
- chore: update maintainers in multiple perception packages
* Revert "chore: update maintainers in multiple perception packages" This reverts commit f2838c33d6cd82bd032039e2a12b9cb8ba6eb584.
- chore: update maintainers in multiple perception packages
* chore: add Kok Seang Tan as maintainer in multiple perception packages ---------
-
feat(autoware_lidar_transfusion): added target architectures for transfusion (#10615)
- chore: added target architectures for transfusion
- chore: removed unnecessary flag
* chore: mistook the architecture of edge devices ---------
-
feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion (#10513)
- feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion
* fix(autoware_lidar_transfusion): clang-tidy errors ---------Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>>
-
Contributors: Amadeusz Szymko, Kenzo Lobos Tsunekawa, Taekjin LEE, TaikiYamada4
0.44.2 (2025-06-10)
0.44.1 (2025-05-01)
0.44.0 (2025-04-18)
-
Merge remote-tracking branch 'origin/main' into humble
-
feat(lidar_transfusion): add diagnostics for processing time (#10398)
- add processing time diagnostics
- style(pre-commit): autofix
* remove comment ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
-
Contributors: Masaki Baba, Ryohsuke Mitsudome
0.43.0 (2025-03-21)
- Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
- chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
- refactor: add autoware_cuda_dependency_meta (#10073)
- Contributors: Esteve Fernandez, Hayato Mizushima, Yutaka Kondo
0.42.0 (2025-03-03)
- Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
- feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
- Contributors: Fumiya Watanabe, 心刚
0.41.2 (2025-02-19)
- chore: bump version to 0.41.1 (#10088)
- Contributors: Ryohsuke Mitsudome
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
Name | Deps |
---|---|
tier4_perception_launch |
Launch files
- launch/lidar_transfusion.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- output/objects [default: objects]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: transfusion]
- model_path [default: $(var data_path)/lidar_transfusion]
- model_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/$(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: $(find-pkg-share autoware_lidar_transfusion)/config/detection_class_remapper.param.yaml]
- common_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/transfusion_common.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_lidar_transfusion at Robotics Stack Exchange
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
Additional Links
Maintainers
- Amadeusz Szymko
- Kenzo Lobos-Tsunekawa
- Kotaro Uetake
- Masato Saeki
- Taekjin Lee
- Kok Seang Tan
Authors
autoware_lidar_transfusion
Purpose
The autoware_lidar_transfusion
package is used for 3D object detection based on lidar data (x, y, z, intensity).
Inner-workings / Algorithms
The implementation bases on TransFusion [1] work. It uses TensorRT library for data process and network inference.
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 (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
TransFusion node
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion.schema.dummy.json”) }}
TransFusion model
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion_ml_package.schema.json”) }}
Detection class remapper
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/detection_class_remapper.schema.json”) }}
The build_only
option
The autoware_lidar_transfusion
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_transfusion lidar_transfusion.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_lidar_transfusion
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_lidar_transfusion lidar_transfusion.launch.xml log_level:=debug
Assumptions / Known limits
This library operates on raw cloud data (bytes). It is assumed that the input pointcloud message has following format:
[
sensor_msgs.msg.PointField(name='x', offset=0, datatype=7, count=1),
sensor_msgs.msg.PointField(name='y', offset=4, datatype=7, count=1),
sensor_msgs.msg.PointField(name='z', offset=8, datatype=7, count=1),
sensor_msgs.msg.PointField(name='intensity', offset=12, datatype=2, count=1)
]
This input may consist of other fields as well - shown format is required minimum. For debug purposes, you can validate your pointcloud topic using simple command:
ros2 topic echo <input_topic> --field fields
Trained Models
You can download the onnx format of trained models by clicking on the links below.
- TransFusion: transfusion.onnx
The model was trained in TIER IV’s internal database (~11k lidar frames) for 50 epochs.
Changelog
(Optional) Error detection and handling
File truncated at 100 lines see the full file
Changelog for package autoware_lidar_transfusion
0.47.0 (2025-08-11)
0.46.0 (2025-06-20)
- Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base
- chore(perception): delete maintainer name (#10816)
- Contributors: Satoshi Tanaka, TaikiYamada4
0.45.0 (2025-05-22)
-
Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
-
chore: perception code owner update (#10645)
- chore: update maintainers in multiple perception packages
* Revert "chore: update maintainers in multiple perception packages" This reverts commit f2838c33d6cd82bd032039e2a12b9cb8ba6eb584.
- chore: update maintainers in multiple perception packages
* chore: add Kok Seang Tan as maintainer in multiple perception packages ---------
-
feat(autoware_lidar_transfusion): added target architectures for transfusion (#10615)
- chore: added target architectures for transfusion
- chore: removed unnecessary flag
* chore: mistook the architecture of edge devices ---------
-
feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion (#10513)
- feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion
* fix(autoware_lidar_transfusion): clang-tidy errors ---------Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>>
-
Contributors: Amadeusz Szymko, Kenzo Lobos Tsunekawa, Taekjin LEE, TaikiYamada4
0.44.2 (2025-06-10)
0.44.1 (2025-05-01)
0.44.0 (2025-04-18)
-
Merge remote-tracking branch 'origin/main' into humble
-
feat(lidar_transfusion): add diagnostics for processing time (#10398)
- add processing time diagnostics
- style(pre-commit): autofix
* remove comment ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
-
Contributors: Masaki Baba, Ryohsuke Mitsudome
0.43.0 (2025-03-21)
- Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
- chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
- refactor: add autoware_cuda_dependency_meta (#10073)
- Contributors: Esteve Fernandez, Hayato Mizushima, Yutaka Kondo
0.42.0 (2025-03-03)
- Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
- feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
- Contributors: Fumiya Watanabe, 心刚
0.41.2 (2025-02-19)
- chore: bump version to 0.41.1 (#10088)
- Contributors: Ryohsuke Mitsudome
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
Name | Deps |
---|---|
tier4_perception_launch |
Launch files
- launch/lidar_transfusion.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- output/objects [default: objects]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: transfusion]
- model_path [default: $(var data_path)/lidar_transfusion]
- model_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/$(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: $(find-pkg-share autoware_lidar_transfusion)/config/detection_class_remapper.param.yaml]
- common_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/transfusion_common.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_lidar_transfusion at Robotics Stack Exchange
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
Additional Links
Maintainers
- Amadeusz Szymko
- Kenzo Lobos-Tsunekawa
- Kotaro Uetake
- Masato Saeki
- Taekjin Lee
- Kok Seang Tan
Authors
autoware_lidar_transfusion
Purpose
The autoware_lidar_transfusion
package is used for 3D object detection based on lidar data (x, y, z, intensity).
Inner-workings / Algorithms
The implementation bases on TransFusion [1] work. It uses TensorRT library for data process and network inference.
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 (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
TransFusion node
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion.schema.dummy.json”) }}
TransFusion model
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion_ml_package.schema.json”) }}
Detection class remapper
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/detection_class_remapper.schema.json”) }}
The build_only
option
The autoware_lidar_transfusion
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_transfusion lidar_transfusion.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_lidar_transfusion
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_lidar_transfusion lidar_transfusion.launch.xml log_level:=debug
Assumptions / Known limits
This library operates on raw cloud data (bytes). It is assumed that the input pointcloud message has following format:
[
sensor_msgs.msg.PointField(name='x', offset=0, datatype=7, count=1),
sensor_msgs.msg.PointField(name='y', offset=4, datatype=7, count=1),
sensor_msgs.msg.PointField(name='z', offset=8, datatype=7, count=1),
sensor_msgs.msg.PointField(name='intensity', offset=12, datatype=2, count=1)
]
This input may consist of other fields as well - shown format is required minimum. For debug purposes, you can validate your pointcloud topic using simple command:
ros2 topic echo <input_topic> --field fields
Trained Models
You can download the onnx format of trained models by clicking on the links below.
- TransFusion: transfusion.onnx
The model was trained in TIER IV’s internal database (~11k lidar frames) for 50 epochs.
Changelog
(Optional) Error detection and handling
File truncated at 100 lines see the full file
Changelog for package autoware_lidar_transfusion
0.47.0 (2025-08-11)
0.46.0 (2025-06-20)
- Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base
- chore(perception): delete maintainer name (#10816)
- Contributors: Satoshi Tanaka, TaikiYamada4
0.45.0 (2025-05-22)
-
Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
-
chore: perception code owner update (#10645)
- chore: update maintainers in multiple perception packages
* Revert "chore: update maintainers in multiple perception packages" This reverts commit f2838c33d6cd82bd032039e2a12b9cb8ba6eb584.
- chore: update maintainers in multiple perception packages
* chore: add Kok Seang Tan as maintainer in multiple perception packages ---------
-
feat(autoware_lidar_transfusion): added target architectures for transfusion (#10615)
- chore: added target architectures for transfusion
- chore: removed unnecessary flag
* chore: mistook the architecture of edge devices ---------
-
feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion (#10513)
- feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion
* fix(autoware_lidar_transfusion): clang-tidy errors ---------Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>>
-
Contributors: Amadeusz Szymko, Kenzo Lobos Tsunekawa, Taekjin LEE, TaikiYamada4
0.44.2 (2025-06-10)
0.44.1 (2025-05-01)
0.44.0 (2025-04-18)
-
Merge remote-tracking branch 'origin/main' into humble
-
feat(lidar_transfusion): add diagnostics for processing time (#10398)
- add processing time diagnostics
- style(pre-commit): autofix
* remove comment ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
-
Contributors: Masaki Baba, Ryohsuke Mitsudome
0.43.0 (2025-03-21)
- Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
- chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
- refactor: add autoware_cuda_dependency_meta (#10073)
- Contributors: Esteve Fernandez, Hayato Mizushima, Yutaka Kondo
0.42.0 (2025-03-03)
- Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
- feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
- Contributors: Fumiya Watanabe, 心刚
0.41.2 (2025-02-19)
- chore: bump version to 0.41.1 (#10088)
- Contributors: Ryohsuke Mitsudome
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
Name | Deps |
---|---|
tier4_perception_launch |
Launch files
- launch/lidar_transfusion.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- output/objects [default: objects]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: transfusion]
- model_path [default: $(var data_path)/lidar_transfusion]
- model_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/$(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: $(find-pkg-share autoware_lidar_transfusion)/config/detection_class_remapper.param.yaml]
- common_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/transfusion_common.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_lidar_transfusion at Robotics Stack Exchange
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
Additional Links
Maintainers
- Amadeusz Szymko
- Kenzo Lobos-Tsunekawa
- Kotaro Uetake
- Masato Saeki
- Taekjin Lee
- Kok Seang Tan
Authors
autoware_lidar_transfusion
Purpose
The autoware_lidar_transfusion
package is used for 3D object detection based on lidar data (x, y, z, intensity).
Inner-workings / Algorithms
The implementation bases on TransFusion [1] work. It uses TensorRT library for data process and network inference.
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 (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
TransFusion node
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion.schema.dummy.json”) }}
TransFusion model
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion_ml_package.schema.json”) }}
Detection class remapper
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/detection_class_remapper.schema.json”) }}
The build_only
option
The autoware_lidar_transfusion
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_transfusion lidar_transfusion.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_lidar_transfusion
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_lidar_transfusion lidar_transfusion.launch.xml log_level:=debug
Assumptions / Known limits
This library operates on raw cloud data (bytes). It is assumed that the input pointcloud message has following format:
[
sensor_msgs.msg.PointField(name='x', offset=0, datatype=7, count=1),
sensor_msgs.msg.PointField(name='y', offset=4, datatype=7, count=1),
sensor_msgs.msg.PointField(name='z', offset=8, datatype=7, count=1),
sensor_msgs.msg.PointField(name='intensity', offset=12, datatype=2, count=1)
]
This input may consist of other fields as well - shown format is required minimum. For debug purposes, you can validate your pointcloud topic using simple command:
ros2 topic echo <input_topic> --field fields
Trained Models
You can download the onnx format of trained models by clicking on the links below.
- TransFusion: transfusion.onnx
The model was trained in TIER IV’s internal database (~11k lidar frames) for 50 epochs.
Changelog
(Optional) Error detection and handling
File truncated at 100 lines see the full file
Changelog for package autoware_lidar_transfusion
0.47.0 (2025-08-11)
0.46.0 (2025-06-20)
- Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base
- chore(perception): delete maintainer name (#10816)
- Contributors: Satoshi Tanaka, TaikiYamada4
0.45.0 (2025-05-22)
-
Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
-
chore: perception code owner update (#10645)
- chore: update maintainers in multiple perception packages
* Revert "chore: update maintainers in multiple perception packages" This reverts commit f2838c33d6cd82bd032039e2a12b9cb8ba6eb584.
- chore: update maintainers in multiple perception packages
* chore: add Kok Seang Tan as maintainer in multiple perception packages ---------
-
feat(autoware_lidar_transfusion): added target architectures for transfusion (#10615)
- chore: added target architectures for transfusion
- chore: removed unnecessary flag
* chore: mistook the architecture of edge devices ---------
-
feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion (#10513)
- feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion
* fix(autoware_lidar_transfusion): clang-tidy errors ---------Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>>
-
Contributors: Amadeusz Szymko, Kenzo Lobos Tsunekawa, Taekjin LEE, TaikiYamada4
0.44.2 (2025-06-10)
0.44.1 (2025-05-01)
0.44.0 (2025-04-18)
-
Merge remote-tracking branch 'origin/main' into humble
-
feat(lidar_transfusion): add diagnostics for processing time (#10398)
- add processing time diagnostics
- style(pre-commit): autofix
* remove comment ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
-
Contributors: Masaki Baba, Ryohsuke Mitsudome
0.43.0 (2025-03-21)
- Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
- chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
- refactor: add autoware_cuda_dependency_meta (#10073)
- Contributors: Esteve Fernandez, Hayato Mizushima, Yutaka Kondo
0.42.0 (2025-03-03)
- Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
- feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
- Contributors: Fumiya Watanabe, 心刚
0.41.2 (2025-02-19)
- chore: bump version to 0.41.1 (#10088)
- Contributors: Ryohsuke Mitsudome
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
Name | Deps |
---|---|
tier4_perception_launch |
Launch files
- launch/lidar_transfusion.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- output/objects [default: objects]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: transfusion]
- model_path [default: $(var data_path)/lidar_transfusion]
- model_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/$(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: $(find-pkg-share autoware_lidar_transfusion)/config/detection_class_remapper.param.yaml]
- common_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/transfusion_common.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_lidar_transfusion at Robotics Stack Exchange
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
Additional Links
Maintainers
- Amadeusz Szymko
- Kenzo Lobos-Tsunekawa
- Kotaro Uetake
- Masato Saeki
- Taekjin Lee
- Kok Seang Tan
Authors
autoware_lidar_transfusion
Purpose
The autoware_lidar_transfusion
package is used for 3D object detection based on lidar data (x, y, z, intensity).
Inner-workings / Algorithms
The implementation bases on TransFusion [1] work. It uses TensorRT library for data process and network inference.
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 (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
TransFusion node
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion.schema.dummy.json”) }}
TransFusion model
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion_ml_package.schema.json”) }}
Detection class remapper
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/detection_class_remapper.schema.json”) }}
The build_only
option
The autoware_lidar_transfusion
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_transfusion lidar_transfusion.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_lidar_transfusion
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_lidar_transfusion lidar_transfusion.launch.xml log_level:=debug
Assumptions / Known limits
This library operates on raw cloud data (bytes). It is assumed that the input pointcloud message has following format:
[
sensor_msgs.msg.PointField(name='x', offset=0, datatype=7, count=1),
sensor_msgs.msg.PointField(name='y', offset=4, datatype=7, count=1),
sensor_msgs.msg.PointField(name='z', offset=8, datatype=7, count=1),
sensor_msgs.msg.PointField(name='intensity', offset=12, datatype=2, count=1)
]
This input may consist of other fields as well - shown format is required minimum. For debug purposes, you can validate your pointcloud topic using simple command:
ros2 topic echo <input_topic> --field fields
Trained Models
You can download the onnx format of trained models by clicking on the links below.
- TransFusion: transfusion.onnx
The model was trained in TIER IV’s internal database (~11k lidar frames) for 50 epochs.
Changelog
(Optional) Error detection and handling
File truncated at 100 lines see the full file
Changelog for package autoware_lidar_transfusion
0.47.0 (2025-08-11)
0.46.0 (2025-06-20)
- Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base
- chore(perception): delete maintainer name (#10816)
- Contributors: Satoshi Tanaka, TaikiYamada4
0.45.0 (2025-05-22)
-
Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
-
chore: perception code owner update (#10645)
- chore: update maintainers in multiple perception packages
* Revert "chore: update maintainers in multiple perception packages" This reverts commit f2838c33d6cd82bd032039e2a12b9cb8ba6eb584.
- chore: update maintainers in multiple perception packages
* chore: add Kok Seang Tan as maintainer in multiple perception packages ---------
-
feat(autoware_lidar_transfusion): added target architectures for transfusion (#10615)
- chore: added target architectures for transfusion
- chore: removed unnecessary flag
* chore: mistook the architecture of edge devices ---------
-
feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion (#10513)
- feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion
* fix(autoware_lidar_transfusion): clang-tidy errors ---------Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>>
-
Contributors: Amadeusz Szymko, Kenzo Lobos Tsunekawa, Taekjin LEE, TaikiYamada4
0.44.2 (2025-06-10)
0.44.1 (2025-05-01)
0.44.0 (2025-04-18)
-
Merge remote-tracking branch 'origin/main' into humble
-
feat(lidar_transfusion): add diagnostics for processing time (#10398)
- add processing time diagnostics
- style(pre-commit): autofix
* remove comment ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
-
Contributors: Masaki Baba, Ryohsuke Mitsudome
0.43.0 (2025-03-21)
- Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
- chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
- refactor: add autoware_cuda_dependency_meta (#10073)
- Contributors: Esteve Fernandez, Hayato Mizushima, Yutaka Kondo
0.42.0 (2025-03-03)
- Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
- feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
- Contributors: Fumiya Watanabe, 心刚
0.41.2 (2025-02-19)
- chore: bump version to 0.41.1 (#10088)
- Contributors: Ryohsuke Mitsudome
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
Name | Deps |
---|---|
tier4_perception_launch |
Launch files
- launch/lidar_transfusion.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- output/objects [default: objects]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: transfusion]
- model_path [default: $(var data_path)/lidar_transfusion]
- model_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/$(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: $(find-pkg-share autoware_lidar_transfusion)/config/detection_class_remapper.param.yaml]
- common_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/transfusion_common.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_lidar_transfusion at Robotics Stack Exchange
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
Additional Links
Maintainers
- Amadeusz Szymko
- Kenzo Lobos-Tsunekawa
- Kotaro Uetake
- Masato Saeki
- Taekjin Lee
- Kok Seang Tan
Authors
autoware_lidar_transfusion
Purpose
The autoware_lidar_transfusion
package is used for 3D object detection based on lidar data (x, y, z, intensity).
Inner-workings / Algorithms
The implementation bases on TransFusion [1] work. It uses TensorRT library for data process and network inference.
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 (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
TransFusion node
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion.schema.dummy.json”) }}
TransFusion model
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/transfusion_ml_package.schema.json”) }}
Detection class remapper
{{ json_to_markdown(“perception/autoware_lidar_transfusion/schema/detection_class_remapper.schema.json”) }}
The build_only
option
The autoware_lidar_transfusion
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_transfusion lidar_transfusion.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_lidar_transfusion
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_lidar_transfusion lidar_transfusion.launch.xml log_level:=debug
Assumptions / Known limits
This library operates on raw cloud data (bytes). It is assumed that the input pointcloud message has following format:
[
sensor_msgs.msg.PointField(name='x', offset=0, datatype=7, count=1),
sensor_msgs.msg.PointField(name='y', offset=4, datatype=7, count=1),
sensor_msgs.msg.PointField(name='z', offset=8, datatype=7, count=1),
sensor_msgs.msg.PointField(name='intensity', offset=12, datatype=2, count=1)
]
This input may consist of other fields as well - shown format is required minimum. For debug purposes, you can validate your pointcloud topic using simple command:
ros2 topic echo <input_topic> --field fields
Trained Models
You can download the onnx format of trained models by clicking on the links below.
- TransFusion: transfusion.onnx
The model was trained in TIER IV’s internal database (~11k lidar frames) for 50 epochs.
Changelog
(Optional) Error detection and handling
File truncated at 100 lines see the full file
Changelog for package autoware_lidar_transfusion
0.47.0 (2025-08-11)
0.46.0 (2025-06-20)
- Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base
- chore(perception): delete maintainer name (#10816)
- Contributors: Satoshi Tanaka, TaikiYamada4
0.45.0 (2025-05-22)
-
Merge remote-tracking branch 'origin/main' into tmp/notbot/bump_version_base
-
chore: perception code owner update (#10645)
- chore: update maintainers in multiple perception packages
* Revert "chore: update maintainers in multiple perception packages" This reverts commit f2838c33d6cd82bd032039e2a12b9cb8ba6eb584.
- chore: update maintainers in multiple perception packages
* chore: add Kok Seang Tan as maintainer in multiple perception packages ---------
-
feat(autoware_lidar_transfusion): added target architectures for transfusion (#10615)
- chore: added target architectures for transfusion
- chore: removed unnecessary flag
* chore: mistook the architecture of edge devices ---------
-
feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion (#10513)
- feat(autoware_lidar_transfusion): introduce cuda blackboard to transfusion
* fix(autoware_lidar_transfusion): clang-tidy errors ---------Co-authored-by: Kenzo Lobos Tsunekawa <<kenzo.lobos@tier4.jp>>
-
Contributors: Amadeusz Szymko, Kenzo Lobos Tsunekawa, Taekjin LEE, TaikiYamada4
0.44.2 (2025-06-10)
0.44.1 (2025-05-01)
0.44.0 (2025-04-18)
-
Merge remote-tracking branch 'origin/main' into humble
-
feat(lidar_transfusion): add diagnostics for processing time (#10398)
- add processing time diagnostics
- style(pre-commit): autofix
* remove comment ---------Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
-
Contributors: Masaki Baba, Ryohsuke Mitsudome
0.43.0 (2025-03-21)
- Merge remote-tracking branch 'origin/main' into chore/bump-version-0.43
- chore: rename from [autoware.universe]{.title-ref} to [autoware_universe]{.title-ref} (#10306)
- refactor: add autoware_cuda_dependency_meta (#10073)
- Contributors: Esteve Fernandez, Hayato Mizushima, Yutaka Kondo
0.42.0 (2025-03-03)
- Merge remote-tracking branch 'origin/main' into tmp/bot/bump_version_base
- feat(autoware_utils): replace autoware_universe_utils with autoware_utils (#10191)
- Contributors: Fumiya Watanabe, 心刚
0.41.2 (2025-02-19)
- chore: bump version to 0.41.1 (#10088)
- Contributors: Ryohsuke Mitsudome
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Dependant Packages
Name | Deps |
---|---|
tier4_perception_launch |
Launch files
- launch/lidar_transfusion.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- output/objects [default: objects]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: transfusion]
- model_path [default: $(var data_path)/lidar_transfusion]
- model_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/$(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: $(find-pkg-share autoware_lidar_transfusion)/config/detection_class_remapper.param.yaml]
- common_param_path [default: $(find-pkg-share autoware_lidar_transfusion)/config/transfusion_common.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]