Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
Authors
- Kosuke Takeuchi
- Yukihiro Saito
lidar_apollo_instance_segmentation
Purpose
This node segments 3D pointcloud data from lidar sensors into obstacles, e.g., cars, trucks, bicycles, and pedestrians based on CNN based model and obstacle clustering method.
Inner-workings / Algorithms
See the original design by Apollo.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
input/pointcloud |
sensor_msgs/PointCloud2 |
Pointcloud data from lidar sensors |
Output
Name | Type | Description |
---|---|---|
output/labeled_clusters |
tier4_perception_msgs/DetectedObjectsWithFeature |
Detected objects with labeled pointcloud cluster. |
debug/instance_pointcloud |
sensor_msgs/PointCloud2 |
Segmented pointcloud for visualization. |
Parameters
Node Parameters
None
Core Parameters
Name | Type | Default Value | Description |
---|---|---|---|
score_threshold |
double | 0.8 | If the score of a detected object is lower than this value, the object is ignored. |
range |
int | 60 | Half of the length of feature map sides. [m] |
width |
int | 640 | The grid width of feature map. |
height |
int | 640 | The grid height of feature map. |
engine_file |
string | “vls-128.engine” | The name of TensorRT engine file for CNN model. |
prototxt_file |
string | “vls-128.prototxt” | The name of prototxt file for CNN model. |
caffemodel_file |
string | “vls-128.caffemodel” | The name of caffemodel file for CNN model. |
use_intensity_feature |
bool | true | The flag to use intensity feature of pointcloud. |
use_constant_feature |
bool | false | The flag to use direction and distance feature of pointcloud. |
target_frame |
string | “base_link” | Pointcloud data is transformed into this frame. |
z_offset |
int | 2 | z offset from target frame. [m] |
Assumptions / Known limits
There is no training code for CNN model.
Note
This package makes use of three external codes. The trained files are provided by apollo. The trained files are automatically downloaded when you build.
Original URL
- VLP-16 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel
- HDL-64 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel
- VLS-128 : https://github.com/ApolloAuto/apollo/raw/91844c80ee4bd0cc838b4de4c625852363c258b5/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne128/deploy.caffemodel
Supported lidars are velodyne 16, 64 and 128, but you can also use velodyne 32 and other lidars with good accuracy.
/******************************************************************************
* Copyright 2017 The Apollo Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*****************************************************************************/
- tensorRTWrapper : It is used under the lib directory.
```txt MIT License
Copyright (c) 2018 lewes6369
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Name |
---|
libpcl-all-dev |
Dependant Packages
Launch files
- launch/lidar_apollo_instance_segmentation.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- model [default: model_128]
- output/objects [default: labeled_clusters]
- base_name [default: vlp-16]
- base_name [default: hdl-64]
- base_name [default: vls-128]
- data_path [default: $(env HOME)/autoware_data]
- trained_onnx_file [default: $(var data_path)/lidar_apollo_instance_segmentation/$(var base_name).onnx]
- param_file [default: $(find-pkg-share lidar_apollo_instance_segmentation)/config/$(var base_name).param.yaml]
- target_frame [default: base_link]
- z_offset [default: -2.0]
- precision [default: fp32]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_instance_segmentation at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
Authors
- Kosuke Takeuchi
- Yukihiro Saito
lidar_apollo_instance_segmentation
Purpose
This node segments 3D pointcloud data from lidar sensors into obstacles, e.g., cars, trucks, bicycles, and pedestrians based on CNN based model and obstacle clustering method.
Inner-workings / Algorithms
See the original design by Apollo.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
input/pointcloud |
sensor_msgs/PointCloud2 |
Pointcloud data from lidar sensors |
Output
Name | Type | Description |
---|---|---|
output/labeled_clusters |
tier4_perception_msgs/DetectedObjectsWithFeature |
Detected objects with labeled pointcloud cluster. |
debug/instance_pointcloud |
sensor_msgs/PointCloud2 |
Segmented pointcloud for visualization. |
Parameters
Node Parameters
None
Core Parameters
Name | Type | Default Value | Description |
---|---|---|---|
score_threshold |
double | 0.8 | If the score of a detected object is lower than this value, the object is ignored. |
range |
int | 60 | Half of the length of feature map sides. [m] |
width |
int | 640 | The grid width of feature map. |
height |
int | 640 | The grid height of feature map. |
engine_file |
string | “vls-128.engine” | The name of TensorRT engine file for CNN model. |
prototxt_file |
string | “vls-128.prototxt” | The name of prototxt file for CNN model. |
caffemodel_file |
string | “vls-128.caffemodel” | The name of caffemodel file for CNN model. |
use_intensity_feature |
bool | true | The flag to use intensity feature of pointcloud. |
use_constant_feature |
bool | false | The flag to use direction and distance feature of pointcloud. |
target_frame |
string | “base_link” | Pointcloud data is transformed into this frame. |
z_offset |
int | 2 | z offset from target frame. [m] |
Assumptions / Known limits
There is no training code for CNN model.
Note
This package makes use of three external codes. The trained files are provided by apollo. The trained files are automatically downloaded when you build.
Original URL
- VLP-16 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel
- HDL-64 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel
- VLS-128 : https://github.com/ApolloAuto/apollo/raw/91844c80ee4bd0cc838b4de4c625852363c258b5/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne128/deploy.caffemodel
Supported lidars are velodyne 16, 64 and 128, but you can also use velodyne 32 and other lidars with good accuracy.
/******************************************************************************
* Copyright 2017 The Apollo Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*****************************************************************************/
- tensorRTWrapper : It is used under the lib directory.
```txt MIT License
Copyright (c) 2018 lewes6369
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Name |
---|
libpcl-all-dev |
Dependant Packages
Launch files
- launch/lidar_apollo_instance_segmentation.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- model [default: model_128]
- output/objects [default: labeled_clusters]
- base_name [default: vlp-16]
- base_name [default: hdl-64]
- base_name [default: vls-128]
- data_path [default: $(env HOME)/autoware_data]
- trained_onnx_file [default: $(var data_path)/lidar_apollo_instance_segmentation/$(var base_name).onnx]
- param_file [default: $(find-pkg-share lidar_apollo_instance_segmentation)/config/$(var base_name).param.yaml]
- target_frame [default: base_link]
- z_offset [default: -2.0]
- precision [default: fp32]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_instance_segmentation at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
Authors
- Kosuke Takeuchi
- Yukihiro Saito
lidar_apollo_instance_segmentation
Purpose
This node segments 3D pointcloud data from lidar sensors into obstacles, e.g., cars, trucks, bicycles, and pedestrians based on CNN based model and obstacle clustering method.
Inner-workings / Algorithms
See the original design by Apollo.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
input/pointcloud |
sensor_msgs/PointCloud2 |
Pointcloud data from lidar sensors |
Output
Name | Type | Description |
---|---|---|
output/labeled_clusters |
tier4_perception_msgs/DetectedObjectsWithFeature |
Detected objects with labeled pointcloud cluster. |
debug/instance_pointcloud |
sensor_msgs/PointCloud2 |
Segmented pointcloud for visualization. |
Parameters
Node Parameters
None
Core Parameters
Name | Type | Default Value | Description |
---|---|---|---|
score_threshold |
double | 0.8 | If the score of a detected object is lower than this value, the object is ignored. |
range |
int | 60 | Half of the length of feature map sides. [m] |
width |
int | 640 | The grid width of feature map. |
height |
int | 640 | The grid height of feature map. |
engine_file |
string | “vls-128.engine” | The name of TensorRT engine file for CNN model. |
prototxt_file |
string | “vls-128.prototxt” | The name of prototxt file for CNN model. |
caffemodel_file |
string | “vls-128.caffemodel” | The name of caffemodel file for CNN model. |
use_intensity_feature |
bool | true | The flag to use intensity feature of pointcloud. |
use_constant_feature |
bool | false | The flag to use direction and distance feature of pointcloud. |
target_frame |
string | “base_link” | Pointcloud data is transformed into this frame. |
z_offset |
int | 2 | z offset from target frame. [m] |
Assumptions / Known limits
There is no training code for CNN model.
Note
This package makes use of three external codes. The trained files are provided by apollo. The trained files are automatically downloaded when you build.
Original URL
- VLP-16 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel
- HDL-64 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel
- VLS-128 : https://github.com/ApolloAuto/apollo/raw/91844c80ee4bd0cc838b4de4c625852363c258b5/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne128/deploy.caffemodel
Supported lidars are velodyne 16, 64 and 128, but you can also use velodyne 32 and other lidars with good accuracy.
/******************************************************************************
* Copyright 2017 The Apollo Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*****************************************************************************/
- tensorRTWrapper : It is used under the lib directory.
```txt MIT License
Copyright (c) 2018 lewes6369
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Name |
---|
libpcl-all-dev |
Dependant Packages
Launch files
- launch/lidar_apollo_instance_segmentation.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- model [default: model_128]
- output/objects [default: labeled_clusters]
- base_name [default: vlp-16]
- base_name [default: hdl-64]
- base_name [default: vls-128]
- data_path [default: $(env HOME)/autoware_data]
- trained_onnx_file [default: $(var data_path)/lidar_apollo_instance_segmentation/$(var base_name).onnx]
- param_file [default: $(find-pkg-share lidar_apollo_instance_segmentation)/config/$(var base_name).param.yaml]
- target_frame [default: base_link]
- z_offset [default: -2.0]
- precision [default: fp32]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_instance_segmentation at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
Authors
- Kosuke Takeuchi
- Yukihiro Saito
lidar_apollo_instance_segmentation
Purpose
This node segments 3D pointcloud data from lidar sensors into obstacles, e.g., cars, trucks, bicycles, and pedestrians based on CNN based model and obstacle clustering method.
Inner-workings / Algorithms
See the original design by Apollo.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
input/pointcloud |
sensor_msgs/PointCloud2 |
Pointcloud data from lidar sensors |
Output
Name | Type | Description |
---|---|---|
output/labeled_clusters |
tier4_perception_msgs/DetectedObjectsWithFeature |
Detected objects with labeled pointcloud cluster. |
debug/instance_pointcloud |
sensor_msgs/PointCloud2 |
Segmented pointcloud for visualization. |
Parameters
Node Parameters
None
Core Parameters
Name | Type | Default Value | Description |
---|---|---|---|
score_threshold |
double | 0.8 | If the score of a detected object is lower than this value, the object is ignored. |
range |
int | 60 | Half of the length of feature map sides. [m] |
width |
int | 640 | The grid width of feature map. |
height |
int | 640 | The grid height of feature map. |
engine_file |
string | “vls-128.engine” | The name of TensorRT engine file for CNN model. |
prototxt_file |
string | “vls-128.prototxt” | The name of prototxt file for CNN model. |
caffemodel_file |
string | “vls-128.caffemodel” | The name of caffemodel file for CNN model. |
use_intensity_feature |
bool | true | The flag to use intensity feature of pointcloud. |
use_constant_feature |
bool | false | The flag to use direction and distance feature of pointcloud. |
target_frame |
string | “base_link” | Pointcloud data is transformed into this frame. |
z_offset |
int | 2 | z offset from target frame. [m] |
Assumptions / Known limits
There is no training code for CNN model.
Note
This package makes use of three external codes. The trained files are provided by apollo. The trained files are automatically downloaded when you build.
Original URL
- VLP-16 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel
- HDL-64 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel
- VLS-128 : https://github.com/ApolloAuto/apollo/raw/91844c80ee4bd0cc838b4de4c625852363c258b5/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne128/deploy.caffemodel
Supported lidars are velodyne 16, 64 and 128, but you can also use velodyne 32 and other lidars with good accuracy.
/******************************************************************************
* Copyright 2017 The Apollo Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*****************************************************************************/
- tensorRTWrapper : It is used under the lib directory.
```txt MIT License
Copyright (c) 2018 lewes6369
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Name |
---|
libpcl-all-dev |
Dependant Packages
Launch files
- launch/lidar_apollo_instance_segmentation.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- model [default: model_128]
- output/objects [default: labeled_clusters]
- base_name [default: vlp-16]
- base_name [default: hdl-64]
- base_name [default: vls-128]
- data_path [default: $(env HOME)/autoware_data]
- trained_onnx_file [default: $(var data_path)/lidar_apollo_instance_segmentation/$(var base_name).onnx]
- param_file [default: $(find-pkg-share lidar_apollo_instance_segmentation)/config/$(var base_name).param.yaml]
- target_frame [default: base_link]
- z_offset [default: -2.0]
- precision [default: fp32]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_instance_segmentation at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
Authors
- Kosuke Takeuchi
- Yukihiro Saito
lidar_apollo_instance_segmentation
Purpose
This node segments 3D pointcloud data from lidar sensors into obstacles, e.g., cars, trucks, bicycles, and pedestrians based on CNN based model and obstacle clustering method.
Inner-workings / Algorithms
See the original design by Apollo.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
input/pointcloud |
sensor_msgs/PointCloud2 |
Pointcloud data from lidar sensors |
Output
Name | Type | Description |
---|---|---|
output/labeled_clusters |
tier4_perception_msgs/DetectedObjectsWithFeature |
Detected objects with labeled pointcloud cluster. |
debug/instance_pointcloud |
sensor_msgs/PointCloud2 |
Segmented pointcloud for visualization. |
Parameters
Node Parameters
None
Core Parameters
Name | Type | Default Value | Description |
---|---|---|---|
score_threshold |
double | 0.8 | If the score of a detected object is lower than this value, the object is ignored. |
range |
int | 60 | Half of the length of feature map sides. [m] |
width |
int | 640 | The grid width of feature map. |
height |
int | 640 | The grid height of feature map. |
engine_file |
string | “vls-128.engine” | The name of TensorRT engine file for CNN model. |
prototxt_file |
string | “vls-128.prototxt” | The name of prototxt file for CNN model. |
caffemodel_file |
string | “vls-128.caffemodel” | The name of caffemodel file for CNN model. |
use_intensity_feature |
bool | true | The flag to use intensity feature of pointcloud. |
use_constant_feature |
bool | false | The flag to use direction and distance feature of pointcloud. |
target_frame |
string | “base_link” | Pointcloud data is transformed into this frame. |
z_offset |
int | 2 | z offset from target frame. [m] |
Assumptions / Known limits
There is no training code for CNN model.
Note
This package makes use of three external codes. The trained files are provided by apollo. The trained files are automatically downloaded when you build.
Original URL
- VLP-16 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel
- HDL-64 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel
- VLS-128 : https://github.com/ApolloAuto/apollo/raw/91844c80ee4bd0cc838b4de4c625852363c258b5/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne128/deploy.caffemodel
Supported lidars are velodyne 16, 64 and 128, but you can also use velodyne 32 and other lidars with good accuracy.
/******************************************************************************
* Copyright 2017 The Apollo Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*****************************************************************************/
- tensorRTWrapper : It is used under the lib directory.
```txt MIT License
Copyright (c) 2018 lewes6369
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Name |
---|
libpcl-all-dev |
Dependant Packages
Launch files
- launch/lidar_apollo_instance_segmentation.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- model [default: model_128]
- output/objects [default: labeled_clusters]
- base_name [default: vlp-16]
- base_name [default: hdl-64]
- base_name [default: vls-128]
- data_path [default: $(env HOME)/autoware_data]
- trained_onnx_file [default: $(var data_path)/lidar_apollo_instance_segmentation/$(var base_name).onnx]
- param_file [default: $(find-pkg-share lidar_apollo_instance_segmentation)/config/$(var base_name).param.yaml]
- target_frame [default: base_link]
- z_offset [default: -2.0]
- precision [default: fp32]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_instance_segmentation at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
Authors
- Kosuke Takeuchi
- Yukihiro Saito
lidar_apollo_instance_segmentation
Purpose
This node segments 3D pointcloud data from lidar sensors into obstacles, e.g., cars, trucks, bicycles, and pedestrians based on CNN based model and obstacle clustering method.
Inner-workings / Algorithms
See the original design by Apollo.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
input/pointcloud |
sensor_msgs/PointCloud2 |
Pointcloud data from lidar sensors |
Output
Name | Type | Description |
---|---|---|
output/labeled_clusters |
tier4_perception_msgs/DetectedObjectsWithFeature |
Detected objects with labeled pointcloud cluster. |
debug/instance_pointcloud |
sensor_msgs/PointCloud2 |
Segmented pointcloud for visualization. |
Parameters
Node Parameters
None
Core Parameters
Name | Type | Default Value | Description |
---|---|---|---|
score_threshold |
double | 0.8 | If the score of a detected object is lower than this value, the object is ignored. |
range |
int | 60 | Half of the length of feature map sides. [m] |
width |
int | 640 | The grid width of feature map. |
height |
int | 640 | The grid height of feature map. |
engine_file |
string | “vls-128.engine” | The name of TensorRT engine file for CNN model. |
prototxt_file |
string | “vls-128.prototxt” | The name of prototxt file for CNN model. |
caffemodel_file |
string | “vls-128.caffemodel” | The name of caffemodel file for CNN model. |
use_intensity_feature |
bool | true | The flag to use intensity feature of pointcloud. |
use_constant_feature |
bool | false | The flag to use direction and distance feature of pointcloud. |
target_frame |
string | “base_link” | Pointcloud data is transformed into this frame. |
z_offset |
int | 2 | z offset from target frame. [m] |
Assumptions / Known limits
There is no training code for CNN model.
Note
This package makes use of three external codes. The trained files are provided by apollo. The trained files are automatically downloaded when you build.
Original URL
- VLP-16 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel
- HDL-64 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel
- VLS-128 : https://github.com/ApolloAuto/apollo/raw/91844c80ee4bd0cc838b4de4c625852363c258b5/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne128/deploy.caffemodel
Supported lidars are velodyne 16, 64 and 128, but you can also use velodyne 32 and other lidars with good accuracy.
/******************************************************************************
* Copyright 2017 The Apollo Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*****************************************************************************/
- tensorRTWrapper : It is used under the lib directory.
```txt MIT License
Copyright (c) 2018 lewes6369
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Name |
---|
libpcl-all-dev |
Dependant Packages
Launch files
- launch/lidar_apollo_instance_segmentation.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- model [default: model_128]
- output/objects [default: labeled_clusters]
- base_name [default: vlp-16]
- base_name [default: hdl-64]
- base_name [default: vls-128]
- data_path [default: $(env HOME)/autoware_data]
- trained_onnx_file [default: $(var data_path)/lidar_apollo_instance_segmentation/$(var base_name).onnx]
- param_file [default: $(find-pkg-share lidar_apollo_instance_segmentation)/config/$(var base_name).param.yaml]
- target_frame [default: base_link]
- z_offset [default: -2.0]
- precision [default: fp32]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_instance_segmentation at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
Authors
- Kosuke Takeuchi
- Yukihiro Saito
lidar_apollo_instance_segmentation
Purpose
This node segments 3D pointcloud data from lidar sensors into obstacles, e.g., cars, trucks, bicycles, and pedestrians based on CNN based model and obstacle clustering method.
Inner-workings / Algorithms
See the original design by Apollo.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
input/pointcloud |
sensor_msgs/PointCloud2 |
Pointcloud data from lidar sensors |
Output
Name | Type | Description |
---|---|---|
output/labeled_clusters |
tier4_perception_msgs/DetectedObjectsWithFeature |
Detected objects with labeled pointcloud cluster. |
debug/instance_pointcloud |
sensor_msgs/PointCloud2 |
Segmented pointcloud for visualization. |
Parameters
Node Parameters
None
Core Parameters
Name | Type | Default Value | Description |
---|---|---|---|
score_threshold |
double | 0.8 | If the score of a detected object is lower than this value, the object is ignored. |
range |
int | 60 | Half of the length of feature map sides. [m] |
width |
int | 640 | The grid width of feature map. |
height |
int | 640 | The grid height of feature map. |
engine_file |
string | “vls-128.engine” | The name of TensorRT engine file for CNN model. |
prototxt_file |
string | “vls-128.prototxt” | The name of prototxt file for CNN model. |
caffemodel_file |
string | “vls-128.caffemodel” | The name of caffemodel file for CNN model. |
use_intensity_feature |
bool | true | The flag to use intensity feature of pointcloud. |
use_constant_feature |
bool | false | The flag to use direction and distance feature of pointcloud. |
target_frame |
string | “base_link” | Pointcloud data is transformed into this frame. |
z_offset |
int | 2 | z offset from target frame. [m] |
Assumptions / Known limits
There is no training code for CNN model.
Note
This package makes use of three external codes. The trained files are provided by apollo. The trained files are automatically downloaded when you build.
Original URL
- VLP-16 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel
- HDL-64 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel
- VLS-128 : https://github.com/ApolloAuto/apollo/raw/91844c80ee4bd0cc838b4de4c625852363c258b5/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne128/deploy.caffemodel
Supported lidars are velodyne 16, 64 and 128, but you can also use velodyne 32 and other lidars with good accuracy.
/******************************************************************************
* Copyright 2017 The Apollo Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*****************************************************************************/
- tensorRTWrapper : It is used under the lib directory.
```txt MIT License
Copyright (c) 2018 lewes6369
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Name |
---|
libpcl-all-dev |
Dependant Packages
Launch files
- launch/lidar_apollo_instance_segmentation.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- model [default: model_128]
- output/objects [default: labeled_clusters]
- base_name [default: vlp-16]
- base_name [default: hdl-64]
- base_name [default: vls-128]
- data_path [default: $(env HOME)/autoware_data]
- trained_onnx_file [default: $(var data_path)/lidar_apollo_instance_segmentation/$(var base_name).onnx]
- param_file [default: $(find-pkg-share lidar_apollo_instance_segmentation)/config/$(var base_name).param.yaml]
- target_frame [default: base_link]
- z_offset [default: -2.0]
- precision [default: fp32]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_instance_segmentation at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
Authors
- Kosuke Takeuchi
- Yukihiro Saito
lidar_apollo_instance_segmentation
Purpose
This node segments 3D pointcloud data from lidar sensors into obstacles, e.g., cars, trucks, bicycles, and pedestrians based on CNN based model and obstacle clustering method.
Inner-workings / Algorithms
See the original design by Apollo.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
input/pointcloud |
sensor_msgs/PointCloud2 |
Pointcloud data from lidar sensors |
Output
Name | Type | Description |
---|---|---|
output/labeled_clusters |
tier4_perception_msgs/DetectedObjectsWithFeature |
Detected objects with labeled pointcloud cluster. |
debug/instance_pointcloud |
sensor_msgs/PointCloud2 |
Segmented pointcloud for visualization. |
Parameters
Node Parameters
None
Core Parameters
Name | Type | Default Value | Description |
---|---|---|---|
score_threshold |
double | 0.8 | If the score of a detected object is lower than this value, the object is ignored. |
range |
int | 60 | Half of the length of feature map sides. [m] |
width |
int | 640 | The grid width of feature map. |
height |
int | 640 | The grid height of feature map. |
engine_file |
string | “vls-128.engine” | The name of TensorRT engine file for CNN model. |
prototxt_file |
string | “vls-128.prototxt” | The name of prototxt file for CNN model. |
caffemodel_file |
string | “vls-128.caffemodel” | The name of caffemodel file for CNN model. |
use_intensity_feature |
bool | true | The flag to use intensity feature of pointcloud. |
use_constant_feature |
bool | false | The flag to use direction and distance feature of pointcloud. |
target_frame |
string | “base_link” | Pointcloud data is transformed into this frame. |
z_offset |
int | 2 | z offset from target frame. [m] |
Assumptions / Known limits
There is no training code for CNN model.
Note
This package makes use of three external codes. The trained files are provided by apollo. The trained files are automatically downloaded when you build.
Original URL
- VLP-16 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel
- HDL-64 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel
- VLS-128 : https://github.com/ApolloAuto/apollo/raw/91844c80ee4bd0cc838b4de4c625852363c258b5/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne128/deploy.caffemodel
Supported lidars are velodyne 16, 64 and 128, but you can also use velodyne 32 and other lidars with good accuracy.
/******************************************************************************
* Copyright 2017 The Apollo Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*****************************************************************************/
- tensorRTWrapper : It is used under the lib directory.
```txt MIT License
Copyright (c) 2018 lewes6369
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Name |
---|
libpcl-all-dev |
Dependant Packages
Launch files
- launch/lidar_apollo_instance_segmentation.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- model [default: model_128]
- output/objects [default: labeled_clusters]
- base_name [default: vlp-16]
- base_name [default: hdl-64]
- base_name [default: vls-128]
- data_path [default: $(env HOME)/autoware_data]
- trained_onnx_file [default: $(var data_path)/lidar_apollo_instance_segmentation/$(var base_name).onnx]
- param_file [default: $(find-pkg-share lidar_apollo_instance_segmentation)/config/$(var base_name).param.yaml]
- target_frame [default: base_link]
- z_offset [default: -2.0]
- precision [default: fp32]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_instance_segmentation at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
Authors
- Kosuke Takeuchi
- Yukihiro Saito
lidar_apollo_instance_segmentation
Purpose
This node segments 3D pointcloud data from lidar sensors into obstacles, e.g., cars, trucks, bicycles, and pedestrians based on CNN based model and obstacle clustering method.
Inner-workings / Algorithms
See the original design by Apollo.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
input/pointcloud |
sensor_msgs/PointCloud2 |
Pointcloud data from lidar sensors |
Output
Name | Type | Description |
---|---|---|
output/labeled_clusters |
tier4_perception_msgs/DetectedObjectsWithFeature |
Detected objects with labeled pointcloud cluster. |
debug/instance_pointcloud |
sensor_msgs/PointCloud2 |
Segmented pointcloud for visualization. |
Parameters
Node Parameters
None
Core Parameters
Name | Type | Default Value | Description |
---|---|---|---|
score_threshold |
double | 0.8 | If the score of a detected object is lower than this value, the object is ignored. |
range |
int | 60 | Half of the length of feature map sides. [m] |
width |
int | 640 | The grid width of feature map. |
height |
int | 640 | The grid height of feature map. |
engine_file |
string | “vls-128.engine” | The name of TensorRT engine file for CNN model. |
prototxt_file |
string | “vls-128.prototxt” | The name of prototxt file for CNN model. |
caffemodel_file |
string | “vls-128.caffemodel” | The name of caffemodel file for CNN model. |
use_intensity_feature |
bool | true | The flag to use intensity feature of pointcloud. |
use_constant_feature |
bool | false | The flag to use direction and distance feature of pointcloud. |
target_frame |
string | “base_link” | Pointcloud data is transformed into this frame. |
z_offset |
int | 2 | z offset from target frame. [m] |
Assumptions / Known limits
There is no training code for CNN model.
Note
This package makes use of three external codes. The trained files are provided by apollo. The trained files are automatically downloaded when you build.
Original URL
- VLP-16 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel
- HDL-64 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel
- VLS-128 : https://github.com/ApolloAuto/apollo/raw/91844c80ee4bd0cc838b4de4c625852363c258b5/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne128/deploy.caffemodel
Supported lidars are velodyne 16, 64 and 128, but you can also use velodyne 32 and other lidars with good accuracy.
/******************************************************************************
* Copyright 2017 The Apollo Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*****************************************************************************/
- tensorRTWrapper : It is used under the lib directory.
```txt MIT License
Copyright (c) 2018 lewes6369
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal
File truncated at 100 lines see the full file
Package Dependencies
System Dependencies
Name |
---|
libpcl-all-dev |
Dependant Packages
Launch files
- launch/lidar_apollo_instance_segmentation.launch.xml
-
- input/pointcloud [default: /sensing/lidar/pointcloud]
- model [default: model_128]
- output/objects [default: labeled_clusters]
- base_name [default: vlp-16]
- base_name [default: hdl-64]
- base_name [default: vls-128]
- data_path [default: $(env HOME)/autoware_data]
- trained_onnx_file [default: $(var data_path)/lidar_apollo_instance_segmentation/$(var base_name).onnx]
- param_file [default: $(find-pkg-share lidar_apollo_instance_segmentation)/config/$(var base_name).param.yaml]
- target_frame [default: base_link]
- z_offset [default: -2.0]
- precision [default: fp32]