Package Summary
Tags | No category tags. |
Version | 1.12.0 |
License | BSD |
Build type | CATKIN |
Use | RECOMMENDED |
Repository Summary
Description | autoware.ai perf |
Checkout URI | https://github.com/is-whale/autoware_learn.git |
VCS Type | git |
VCS Version | 1.14 |
Last Updated | 2025-03-14 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kosuke Murakami
Authors
- Kosuke Murakami
CNN LiDAR Baidu Object Segmenter
Autoware package based on Baidu’s object segmenter.
Pre requisites
Caffe distributable installed in your home (~/caffe/distribute
).
$ cd
$ git clone https://github.com/BVLC/caffe
$ cd caffe
Follow instructions from Installing Caffe from source.
- Use offical Make compilation procedure.
- Do not use thirdparty CMake setup.
Compile and create distributable:
$ make
$ make distribute
Recompile Autoware to build the node.
The Pretrained model
Use this link to download the pretrained model from Baidu:
https://github.com/ApolloAuto/apollo/tree/v5.5.0/modules/perception/production/data/perception/lidar/models/cnnseg
These two files are needed:
- deploy.prototxt
- deploy.caffemodel
How to launch
- From a sourced terminal:
Using rosrun:
rosrun lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect _network_definition_file:=/PATH/TO/FILE.prototxt _pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel _points_src:=/points_raw
Using launch file:
roslaunch lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect.launch network_definition_file:=/PATH/TO/FILE.prototxt pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel points_src:=/points_raw
- From Runtime Manager:
Computing Tab -> Detection/ lidar_detector -> lidar_cnn_baidu_detect
. Configure parameters using the [app]
button.
Parameters
Parameter | Type | Description | Default |
---|---|---|---|
network_definition_file |
String | Path to the network definition file (prototxt) | |
pretrained_model_file |
String | Path to the Pretrained model (weights) | |
points_src |
String | Input topic Pointcloud. Default. | /points_raw |
score_threshold |
Double | Minimum score required as given by the network to include the result (0.-1.) | 0.6 |
use_gpu |
Bool | Whether ot not to use a GPU device | true |
gpu_device_id |
Int | GPU ID | 0 |
width |
Int | Width of the 2d cluster | 512 |
height |
Int | Height of the 2d cluster | 512 |
range |
Int | Range for the 2d cluster | 60 |
use_constant_feature |
Bool | Use constant model feature (8 features) | false |
normalize_lidar_intensity |
Bool | Normalize the received lidar intensity data | false |
Outputs
Topic | Type | Description |
---|---|---|
/detection/lidar_detector/points_cluster |
sensor_msgs/PointCloud2 |
Colored PointCloud of the resulting detected objects |
/detection/lidar_detector/objects |
autoware_msgs/DetectedObjetArray |
Array of Detected Objects in Autoware format |
Notes
-
To display the results in Rviz
objects_visualizer
is required. (Launch file launches automatically this node). -
Pre trained models can be downloaded from the Apollo project repository.
Changelog for package lidar_apollo_cnn_seg_detect
1.11.0 (2019-03-21)
- [feature] Baidu's CNN based LiDAR segmentation
(#1800)
-
Add build caffe
-
add include apollo files
-
add apollo cnn
-
calculating time
-
- Cleaned node
-
Parametrized inputs
-
Works with custom caffe
-
- Parameterized
-
Works with custom Caffe
-
Removed hard coded params
-
Cleaned up dependencies
-
Added bboxes, labels
-
Minor fixes
-
Custom input topic
-
Added UI, launch file, readme
-
Added Compatibility for Perception Cleanup
-
- Added license messages
-
Updated readme
-
Added extra instructions
- Fix markdown
-
- Contributors: Abraham Monrroy Cano
Package Dependencies
Deps | Name |
---|---|
autoware_build_flags | |
catkin | |
autoware_msgs | |
geometry_msgs | |
pcl_ros | |
roscpp | |
sensor_msgs | |
tf | |
tf_conversions |
System Dependencies
Dependant Packages
Launch files
- launch/lidar_apollo_cnn_seg_detect.launch
-
- network_definition_file
- pretrained_model_file
- points_src [default: /points_raw]
- score_threshold [default: 0.6]
- use_gpu [default: true]
- gpu_device_id [default: 0]
- width [default: 512]
- height [default: 512]
- range [default: 60]
- use_constant_feature [default: false]
- normalize_lidar_intensity [default: false]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_cnn_seg_detect at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 1.12.0 |
License | BSD |
Build type | CATKIN |
Use | RECOMMENDED |
Repository Summary
Description | autoware.ai perf |
Checkout URI | https://github.com/is-whale/autoware_learn.git |
VCS Type | git |
VCS Version | 1.14 |
Last Updated | 2025-03-14 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kosuke Murakami
Authors
- Kosuke Murakami
CNN LiDAR Baidu Object Segmenter
Autoware package based on Baidu’s object segmenter.
Pre requisites
Caffe distributable installed in your home (~/caffe/distribute
).
$ cd
$ git clone https://github.com/BVLC/caffe
$ cd caffe
Follow instructions from Installing Caffe from source.
- Use offical Make compilation procedure.
- Do not use thirdparty CMake setup.
Compile and create distributable:
$ make
$ make distribute
Recompile Autoware to build the node.
The Pretrained model
Use this link to download the pretrained model from Baidu:
https://github.com/ApolloAuto/apollo/tree/v5.5.0/modules/perception/production/data/perception/lidar/models/cnnseg
These two files are needed:
- deploy.prototxt
- deploy.caffemodel
How to launch
- From a sourced terminal:
Using rosrun:
rosrun lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect _network_definition_file:=/PATH/TO/FILE.prototxt _pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel _points_src:=/points_raw
Using launch file:
roslaunch lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect.launch network_definition_file:=/PATH/TO/FILE.prototxt pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel points_src:=/points_raw
- From Runtime Manager:
Computing Tab -> Detection/ lidar_detector -> lidar_cnn_baidu_detect
. Configure parameters using the [app]
button.
Parameters
Parameter | Type | Description | Default |
---|---|---|---|
network_definition_file |
String | Path to the network definition file (prototxt) | |
pretrained_model_file |
String | Path to the Pretrained model (weights) | |
points_src |
String | Input topic Pointcloud. Default. | /points_raw |
score_threshold |
Double | Minimum score required as given by the network to include the result (0.-1.) | 0.6 |
use_gpu |
Bool | Whether ot not to use a GPU device | true |
gpu_device_id |
Int | GPU ID | 0 |
width |
Int | Width of the 2d cluster | 512 |
height |
Int | Height of the 2d cluster | 512 |
range |
Int | Range for the 2d cluster | 60 |
use_constant_feature |
Bool | Use constant model feature (8 features) | false |
normalize_lidar_intensity |
Bool | Normalize the received lidar intensity data | false |
Outputs
Topic | Type | Description |
---|---|---|
/detection/lidar_detector/points_cluster |
sensor_msgs/PointCloud2 |
Colored PointCloud of the resulting detected objects |
/detection/lidar_detector/objects |
autoware_msgs/DetectedObjetArray |
Array of Detected Objects in Autoware format |
Notes
-
To display the results in Rviz
objects_visualizer
is required. (Launch file launches automatically this node). -
Pre trained models can be downloaded from the Apollo project repository.
Changelog for package lidar_apollo_cnn_seg_detect
1.11.0 (2019-03-21)
- [feature] Baidu's CNN based LiDAR segmentation
(#1800)
-
Add build caffe
-
add include apollo files
-
add apollo cnn
-
calculating time
-
- Cleaned node
-
Parametrized inputs
-
Works with custom caffe
-
- Parameterized
-
Works with custom Caffe
-
Removed hard coded params
-
Cleaned up dependencies
-
Added bboxes, labels
-
Minor fixes
-
Custom input topic
-
Added UI, launch file, readme
-
Added Compatibility for Perception Cleanup
-
- Added license messages
-
Updated readme
-
Added extra instructions
- Fix markdown
-
- Contributors: Abraham Monrroy Cano
Package Dependencies
Deps | Name |
---|---|
autoware_build_flags | |
catkin | |
autoware_msgs | |
geometry_msgs | |
pcl_ros | |
roscpp | |
sensor_msgs | |
tf | |
tf_conversions |
System Dependencies
Dependant Packages
Launch files
- launch/lidar_apollo_cnn_seg_detect.launch
-
- network_definition_file
- pretrained_model_file
- points_src [default: /points_raw]
- score_threshold [default: 0.6]
- use_gpu [default: true]
- gpu_device_id [default: 0]
- width [default: 512]
- height [default: 512]
- range [default: 60]
- use_constant_feature [default: false]
- normalize_lidar_intensity [default: false]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_cnn_seg_detect at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 1.12.0 |
License | BSD |
Build type | CATKIN |
Use | RECOMMENDED |
Repository Summary
Description | autoware.ai perf |
Checkout URI | https://github.com/is-whale/autoware_learn.git |
VCS Type | git |
VCS Version | 1.14 |
Last Updated | 2025-03-14 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kosuke Murakami
Authors
- Kosuke Murakami
CNN LiDAR Baidu Object Segmenter
Autoware package based on Baidu’s object segmenter.
Pre requisites
Caffe distributable installed in your home (~/caffe/distribute
).
$ cd
$ git clone https://github.com/BVLC/caffe
$ cd caffe
Follow instructions from Installing Caffe from source.
- Use offical Make compilation procedure.
- Do not use thirdparty CMake setup.
Compile and create distributable:
$ make
$ make distribute
Recompile Autoware to build the node.
The Pretrained model
Use this link to download the pretrained model from Baidu:
https://github.com/ApolloAuto/apollo/tree/v5.5.0/modules/perception/production/data/perception/lidar/models/cnnseg
These two files are needed:
- deploy.prototxt
- deploy.caffemodel
How to launch
- From a sourced terminal:
Using rosrun:
rosrun lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect _network_definition_file:=/PATH/TO/FILE.prototxt _pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel _points_src:=/points_raw
Using launch file:
roslaunch lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect.launch network_definition_file:=/PATH/TO/FILE.prototxt pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel points_src:=/points_raw
- From Runtime Manager:
Computing Tab -> Detection/ lidar_detector -> lidar_cnn_baidu_detect
. Configure parameters using the [app]
button.
Parameters
Parameter | Type | Description | Default |
---|---|---|---|
network_definition_file |
String | Path to the network definition file (prototxt) | |
pretrained_model_file |
String | Path to the Pretrained model (weights) | |
points_src |
String | Input topic Pointcloud. Default. | /points_raw |
score_threshold |
Double | Minimum score required as given by the network to include the result (0.-1.) | 0.6 |
use_gpu |
Bool | Whether ot not to use a GPU device | true |
gpu_device_id |
Int | GPU ID | 0 |
width |
Int | Width of the 2d cluster | 512 |
height |
Int | Height of the 2d cluster | 512 |
range |
Int | Range for the 2d cluster | 60 |
use_constant_feature |
Bool | Use constant model feature (8 features) | false |
normalize_lidar_intensity |
Bool | Normalize the received lidar intensity data | false |
Outputs
Topic | Type | Description |
---|---|---|
/detection/lidar_detector/points_cluster |
sensor_msgs/PointCloud2 |
Colored PointCloud of the resulting detected objects |
/detection/lidar_detector/objects |
autoware_msgs/DetectedObjetArray |
Array of Detected Objects in Autoware format |
Notes
-
To display the results in Rviz
objects_visualizer
is required. (Launch file launches automatically this node). -
Pre trained models can be downloaded from the Apollo project repository.
Changelog for package lidar_apollo_cnn_seg_detect
1.11.0 (2019-03-21)
- [feature] Baidu's CNN based LiDAR segmentation
(#1800)
-
Add build caffe
-
add include apollo files
-
add apollo cnn
-
calculating time
-
- Cleaned node
-
Parametrized inputs
-
Works with custom caffe
-
- Parameterized
-
Works with custom Caffe
-
Removed hard coded params
-
Cleaned up dependencies
-
Added bboxes, labels
-
Minor fixes
-
Custom input topic
-
Added UI, launch file, readme
-
Added Compatibility for Perception Cleanup
-
- Added license messages
-
Updated readme
-
Added extra instructions
- Fix markdown
-
- Contributors: Abraham Monrroy Cano
Package Dependencies
Deps | Name |
---|---|
autoware_build_flags | |
catkin | |
autoware_msgs | |
geometry_msgs | |
pcl_ros | |
roscpp | |
sensor_msgs | |
tf | |
tf_conversions |
System Dependencies
Dependant Packages
Launch files
- launch/lidar_apollo_cnn_seg_detect.launch
-
- network_definition_file
- pretrained_model_file
- points_src [default: /points_raw]
- score_threshold [default: 0.6]
- use_gpu [default: true]
- gpu_device_id [default: 0]
- width [default: 512]
- height [default: 512]
- range [default: 60]
- use_constant_feature [default: false]
- normalize_lidar_intensity [default: false]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_cnn_seg_detect at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 1.12.0 |
License | BSD |
Build type | CATKIN |
Use | RECOMMENDED |
Repository Summary
Description | autoware.ai perf |
Checkout URI | https://github.com/is-whale/autoware_learn.git |
VCS Type | git |
VCS Version | 1.14 |
Last Updated | 2025-03-14 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kosuke Murakami
Authors
- Kosuke Murakami
CNN LiDAR Baidu Object Segmenter
Autoware package based on Baidu’s object segmenter.
Pre requisites
Caffe distributable installed in your home (~/caffe/distribute
).
$ cd
$ git clone https://github.com/BVLC/caffe
$ cd caffe
Follow instructions from Installing Caffe from source.
- Use offical Make compilation procedure.
- Do not use thirdparty CMake setup.
Compile and create distributable:
$ make
$ make distribute
Recompile Autoware to build the node.
The Pretrained model
Use this link to download the pretrained model from Baidu:
https://github.com/ApolloAuto/apollo/tree/v5.5.0/modules/perception/production/data/perception/lidar/models/cnnseg
These two files are needed:
- deploy.prototxt
- deploy.caffemodel
How to launch
- From a sourced terminal:
Using rosrun:
rosrun lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect _network_definition_file:=/PATH/TO/FILE.prototxt _pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel _points_src:=/points_raw
Using launch file:
roslaunch lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect.launch network_definition_file:=/PATH/TO/FILE.prototxt pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel points_src:=/points_raw
- From Runtime Manager:
Computing Tab -> Detection/ lidar_detector -> lidar_cnn_baidu_detect
. Configure parameters using the [app]
button.
Parameters
Parameter | Type | Description | Default |
---|---|---|---|
network_definition_file |
String | Path to the network definition file (prototxt) | |
pretrained_model_file |
String | Path to the Pretrained model (weights) | |
points_src |
String | Input topic Pointcloud. Default. | /points_raw |
score_threshold |
Double | Minimum score required as given by the network to include the result (0.-1.) | 0.6 |
use_gpu |
Bool | Whether ot not to use a GPU device | true |
gpu_device_id |
Int | GPU ID | 0 |
width |
Int | Width of the 2d cluster | 512 |
height |
Int | Height of the 2d cluster | 512 |
range |
Int | Range for the 2d cluster | 60 |
use_constant_feature |
Bool | Use constant model feature (8 features) | false |
normalize_lidar_intensity |
Bool | Normalize the received lidar intensity data | false |
Outputs
Topic | Type | Description |
---|---|---|
/detection/lidar_detector/points_cluster |
sensor_msgs/PointCloud2 |
Colored PointCloud of the resulting detected objects |
/detection/lidar_detector/objects |
autoware_msgs/DetectedObjetArray |
Array of Detected Objects in Autoware format |
Notes
-
To display the results in Rviz
objects_visualizer
is required. (Launch file launches automatically this node). -
Pre trained models can be downloaded from the Apollo project repository.
Changelog for package lidar_apollo_cnn_seg_detect
1.11.0 (2019-03-21)
- [feature] Baidu's CNN based LiDAR segmentation
(#1800)
-
Add build caffe
-
add include apollo files
-
add apollo cnn
-
calculating time
-
- Cleaned node
-
Parametrized inputs
-
Works with custom caffe
-
- Parameterized
-
Works with custom Caffe
-
Removed hard coded params
-
Cleaned up dependencies
-
Added bboxes, labels
-
Minor fixes
-
Custom input topic
-
Added UI, launch file, readme
-
Added Compatibility for Perception Cleanup
-
- Added license messages
-
Updated readme
-
Added extra instructions
- Fix markdown
-
- Contributors: Abraham Monrroy Cano
Package Dependencies
Deps | Name |
---|---|
autoware_build_flags | |
catkin | |
autoware_msgs | |
geometry_msgs | |
pcl_ros | |
roscpp | |
sensor_msgs | |
tf | |
tf_conversions |
System Dependencies
Dependant Packages
Launch files
- launch/lidar_apollo_cnn_seg_detect.launch
-
- network_definition_file
- pretrained_model_file
- points_src [default: /points_raw]
- score_threshold [default: 0.6]
- use_gpu [default: true]
- gpu_device_id [default: 0]
- width [default: 512]
- height [default: 512]
- range [default: 60]
- use_constant_feature [default: false]
- normalize_lidar_intensity [default: false]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_cnn_seg_detect at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 1.12.0 |
License | BSD |
Build type | CATKIN |
Use | RECOMMENDED |
Repository Summary
Description | autoware.ai perf |
Checkout URI | https://github.com/is-whale/autoware_learn.git |
VCS Type | git |
VCS Version | 1.14 |
Last Updated | 2025-03-14 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kosuke Murakami
Authors
- Kosuke Murakami
CNN LiDAR Baidu Object Segmenter
Autoware package based on Baidu’s object segmenter.
Pre requisites
Caffe distributable installed in your home (~/caffe/distribute
).
$ cd
$ git clone https://github.com/BVLC/caffe
$ cd caffe
Follow instructions from Installing Caffe from source.
- Use offical Make compilation procedure.
- Do not use thirdparty CMake setup.
Compile and create distributable:
$ make
$ make distribute
Recompile Autoware to build the node.
The Pretrained model
Use this link to download the pretrained model from Baidu:
https://github.com/ApolloAuto/apollo/tree/v5.5.0/modules/perception/production/data/perception/lidar/models/cnnseg
These two files are needed:
- deploy.prototxt
- deploy.caffemodel
How to launch
- From a sourced terminal:
Using rosrun:
rosrun lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect _network_definition_file:=/PATH/TO/FILE.prototxt _pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel _points_src:=/points_raw
Using launch file:
roslaunch lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect.launch network_definition_file:=/PATH/TO/FILE.prototxt pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel points_src:=/points_raw
- From Runtime Manager:
Computing Tab -> Detection/ lidar_detector -> lidar_cnn_baidu_detect
. Configure parameters using the [app]
button.
Parameters
Parameter | Type | Description | Default |
---|---|---|---|
network_definition_file |
String | Path to the network definition file (prototxt) | |
pretrained_model_file |
String | Path to the Pretrained model (weights) | |
points_src |
String | Input topic Pointcloud. Default. | /points_raw |
score_threshold |
Double | Minimum score required as given by the network to include the result (0.-1.) | 0.6 |
use_gpu |
Bool | Whether ot not to use a GPU device | true |
gpu_device_id |
Int | GPU ID | 0 |
width |
Int | Width of the 2d cluster | 512 |
height |
Int | Height of the 2d cluster | 512 |
range |
Int | Range for the 2d cluster | 60 |
use_constant_feature |
Bool | Use constant model feature (8 features) | false |
normalize_lidar_intensity |
Bool | Normalize the received lidar intensity data | false |
Outputs
Topic | Type | Description |
---|---|---|
/detection/lidar_detector/points_cluster |
sensor_msgs/PointCloud2 |
Colored PointCloud of the resulting detected objects |
/detection/lidar_detector/objects |
autoware_msgs/DetectedObjetArray |
Array of Detected Objects in Autoware format |
Notes
-
To display the results in Rviz
objects_visualizer
is required. (Launch file launches automatically this node). -
Pre trained models can be downloaded from the Apollo project repository.
Changelog for package lidar_apollo_cnn_seg_detect
1.11.0 (2019-03-21)
- [feature] Baidu's CNN based LiDAR segmentation
(#1800)
-
Add build caffe
-
add include apollo files
-
add apollo cnn
-
calculating time
-
- Cleaned node
-
Parametrized inputs
-
Works with custom caffe
-
- Parameterized
-
Works with custom Caffe
-
Removed hard coded params
-
Cleaned up dependencies
-
Added bboxes, labels
-
Minor fixes
-
Custom input topic
-
Added UI, launch file, readme
-
Added Compatibility for Perception Cleanup
-
- Added license messages
-
Updated readme
-
Added extra instructions
- Fix markdown
-
- Contributors: Abraham Monrroy Cano
Package Dependencies
Deps | Name |
---|---|
autoware_build_flags | |
catkin | |
autoware_msgs | |
geometry_msgs | |
pcl_ros | |
roscpp | |
sensor_msgs | |
tf | |
tf_conversions |
System Dependencies
Dependant Packages
Launch files
- launch/lidar_apollo_cnn_seg_detect.launch
-
- network_definition_file
- pretrained_model_file
- points_src [default: /points_raw]
- score_threshold [default: 0.6]
- use_gpu [default: true]
- gpu_device_id [default: 0]
- width [default: 512]
- height [default: 512]
- range [default: 60]
- use_constant_feature [default: false]
- normalize_lidar_intensity [default: false]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_cnn_seg_detect at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 1.12.0 |
License | BSD |
Build type | CATKIN |
Use | RECOMMENDED |
Repository Summary
Description | autoware.ai perf |
Checkout URI | https://github.com/is-whale/autoware_learn.git |
VCS Type | git |
VCS Version | 1.14 |
Last Updated | 2025-03-14 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kosuke Murakami
Authors
- Kosuke Murakami
CNN LiDAR Baidu Object Segmenter
Autoware package based on Baidu’s object segmenter.
Pre requisites
Caffe distributable installed in your home (~/caffe/distribute
).
$ cd
$ git clone https://github.com/BVLC/caffe
$ cd caffe
Follow instructions from Installing Caffe from source.
- Use offical Make compilation procedure.
- Do not use thirdparty CMake setup.
Compile and create distributable:
$ make
$ make distribute
Recompile Autoware to build the node.
The Pretrained model
Use this link to download the pretrained model from Baidu:
https://github.com/ApolloAuto/apollo/tree/v5.5.0/modules/perception/production/data/perception/lidar/models/cnnseg
These two files are needed:
- deploy.prototxt
- deploy.caffemodel
How to launch
- From a sourced terminal:
Using rosrun:
rosrun lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect _network_definition_file:=/PATH/TO/FILE.prototxt _pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel _points_src:=/points_raw
Using launch file:
roslaunch lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect.launch network_definition_file:=/PATH/TO/FILE.prototxt pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel points_src:=/points_raw
- From Runtime Manager:
Computing Tab -> Detection/ lidar_detector -> lidar_cnn_baidu_detect
. Configure parameters using the [app]
button.
Parameters
Parameter | Type | Description | Default |
---|---|---|---|
network_definition_file |
String | Path to the network definition file (prototxt) | |
pretrained_model_file |
String | Path to the Pretrained model (weights) | |
points_src |
String | Input topic Pointcloud. Default. | /points_raw |
score_threshold |
Double | Minimum score required as given by the network to include the result (0.-1.) | 0.6 |
use_gpu |
Bool | Whether ot not to use a GPU device | true |
gpu_device_id |
Int | GPU ID | 0 |
width |
Int | Width of the 2d cluster | 512 |
height |
Int | Height of the 2d cluster | 512 |
range |
Int | Range for the 2d cluster | 60 |
use_constant_feature |
Bool | Use constant model feature (8 features) | false |
normalize_lidar_intensity |
Bool | Normalize the received lidar intensity data | false |
Outputs
Topic | Type | Description |
---|---|---|
/detection/lidar_detector/points_cluster |
sensor_msgs/PointCloud2 |
Colored PointCloud of the resulting detected objects |
/detection/lidar_detector/objects |
autoware_msgs/DetectedObjetArray |
Array of Detected Objects in Autoware format |
Notes
-
To display the results in Rviz
objects_visualizer
is required. (Launch file launches automatically this node). -
Pre trained models can be downloaded from the Apollo project repository.
Changelog for package lidar_apollo_cnn_seg_detect
1.11.0 (2019-03-21)
- [feature] Baidu's CNN based LiDAR segmentation
(#1800)
-
Add build caffe
-
add include apollo files
-
add apollo cnn
-
calculating time
-
- Cleaned node
-
Parametrized inputs
-
Works with custom caffe
-
- Parameterized
-
Works with custom Caffe
-
Removed hard coded params
-
Cleaned up dependencies
-
Added bboxes, labels
-
Minor fixes
-
Custom input topic
-
Added UI, launch file, readme
-
Added Compatibility for Perception Cleanup
-
- Added license messages
-
Updated readme
-
Added extra instructions
- Fix markdown
-
- Contributors: Abraham Monrroy Cano
Package Dependencies
Deps | Name |
---|---|
autoware_build_flags | |
catkin | |
autoware_msgs | |
geometry_msgs | |
pcl_ros | |
roscpp | |
sensor_msgs | |
tf | |
tf_conversions |
System Dependencies
Dependant Packages
Launch files
- launch/lidar_apollo_cnn_seg_detect.launch
-
- network_definition_file
- pretrained_model_file
- points_src [default: /points_raw]
- score_threshold [default: 0.6]
- use_gpu [default: true]
- gpu_device_id [default: 0]
- width [default: 512]
- height [default: 512]
- range [default: 60]
- use_constant_feature [default: false]
- normalize_lidar_intensity [default: false]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_cnn_seg_detect at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 1.12.0 |
License | BSD |
Build type | CATKIN |
Use | RECOMMENDED |
Repository Summary
Description | autoware.ai perf |
Checkout URI | https://github.com/is-whale/autoware_learn.git |
VCS Type | git |
VCS Version | 1.14 |
Last Updated | 2025-03-14 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kosuke Murakami
Authors
- Kosuke Murakami
CNN LiDAR Baidu Object Segmenter
Autoware package based on Baidu’s object segmenter.
Pre requisites
Caffe distributable installed in your home (~/caffe/distribute
).
$ cd
$ git clone https://github.com/BVLC/caffe
$ cd caffe
Follow instructions from Installing Caffe from source.
- Use offical Make compilation procedure.
- Do not use thirdparty CMake setup.
Compile and create distributable:
$ make
$ make distribute
Recompile Autoware to build the node.
The Pretrained model
Use this link to download the pretrained model from Baidu:
https://github.com/ApolloAuto/apollo/tree/v5.5.0/modules/perception/production/data/perception/lidar/models/cnnseg
These two files are needed:
- deploy.prototxt
- deploy.caffemodel
How to launch
- From a sourced terminal:
Using rosrun:
rosrun lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect _network_definition_file:=/PATH/TO/FILE.prototxt _pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel _points_src:=/points_raw
Using launch file:
roslaunch lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect.launch network_definition_file:=/PATH/TO/FILE.prototxt pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel points_src:=/points_raw
- From Runtime Manager:
Computing Tab -> Detection/ lidar_detector -> lidar_cnn_baidu_detect
. Configure parameters using the [app]
button.
Parameters
Parameter | Type | Description | Default |
---|---|---|---|
network_definition_file |
String | Path to the network definition file (prototxt) | |
pretrained_model_file |
String | Path to the Pretrained model (weights) | |
points_src |
String | Input topic Pointcloud. Default. | /points_raw |
score_threshold |
Double | Minimum score required as given by the network to include the result (0.-1.) | 0.6 |
use_gpu |
Bool | Whether ot not to use a GPU device | true |
gpu_device_id |
Int | GPU ID | 0 |
width |
Int | Width of the 2d cluster | 512 |
height |
Int | Height of the 2d cluster | 512 |
range |
Int | Range for the 2d cluster | 60 |
use_constant_feature |
Bool | Use constant model feature (8 features) | false |
normalize_lidar_intensity |
Bool | Normalize the received lidar intensity data | false |
Outputs
Topic | Type | Description |
---|---|---|
/detection/lidar_detector/points_cluster |
sensor_msgs/PointCloud2 |
Colored PointCloud of the resulting detected objects |
/detection/lidar_detector/objects |
autoware_msgs/DetectedObjetArray |
Array of Detected Objects in Autoware format |
Notes
-
To display the results in Rviz
objects_visualizer
is required. (Launch file launches automatically this node). -
Pre trained models can be downloaded from the Apollo project repository.
Changelog for package lidar_apollo_cnn_seg_detect
1.11.0 (2019-03-21)
- [feature] Baidu's CNN based LiDAR segmentation
(#1800)
-
Add build caffe
-
add include apollo files
-
add apollo cnn
-
calculating time
-
- Cleaned node
-
Parametrized inputs
-
Works with custom caffe
-
- Parameterized
-
Works with custom Caffe
-
Removed hard coded params
-
Cleaned up dependencies
-
Added bboxes, labels
-
Minor fixes
-
Custom input topic
-
Added UI, launch file, readme
-
Added Compatibility for Perception Cleanup
-
- Added license messages
-
Updated readme
-
Added extra instructions
- Fix markdown
-
- Contributors: Abraham Monrroy Cano
Package Dependencies
Deps | Name |
---|---|
autoware_build_flags | |
catkin | |
autoware_msgs | |
geometry_msgs | |
pcl_ros | |
roscpp | |
sensor_msgs | |
tf | |
tf_conversions |
System Dependencies
Dependant Packages
Launch files
- launch/lidar_apollo_cnn_seg_detect.launch
-
- network_definition_file
- pretrained_model_file
- points_src [default: /points_raw]
- score_threshold [default: 0.6]
- use_gpu [default: true]
- gpu_device_id [default: 0]
- width [default: 512]
- height [default: 512]
- range [default: 60]
- use_constant_feature [default: false]
- normalize_lidar_intensity [default: false]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_cnn_seg_detect at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 1.12.0 |
License | BSD |
Build type | CATKIN |
Use | RECOMMENDED |
Repository Summary
Description | autoware.ai perf |
Checkout URI | https://github.com/is-whale/autoware_learn.git |
VCS Type | git |
VCS Version | 1.14 |
Last Updated | 2025-03-14 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kosuke Murakami
Authors
- Kosuke Murakami
CNN LiDAR Baidu Object Segmenter
Autoware package based on Baidu’s object segmenter.
Pre requisites
Caffe distributable installed in your home (~/caffe/distribute
).
$ cd
$ git clone https://github.com/BVLC/caffe
$ cd caffe
Follow instructions from Installing Caffe from source.
- Use offical Make compilation procedure.
- Do not use thirdparty CMake setup.
Compile and create distributable:
$ make
$ make distribute
Recompile Autoware to build the node.
The Pretrained model
Use this link to download the pretrained model from Baidu:
https://github.com/ApolloAuto/apollo/tree/v5.5.0/modules/perception/production/data/perception/lidar/models/cnnseg
These two files are needed:
- deploy.prototxt
- deploy.caffemodel
How to launch
- From a sourced terminal:
Using rosrun:
rosrun lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect _network_definition_file:=/PATH/TO/FILE.prototxt _pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel _points_src:=/points_raw
Using launch file:
roslaunch lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect.launch network_definition_file:=/PATH/TO/FILE.prototxt pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel points_src:=/points_raw
- From Runtime Manager:
Computing Tab -> Detection/ lidar_detector -> lidar_cnn_baidu_detect
. Configure parameters using the [app]
button.
Parameters
Parameter | Type | Description | Default |
---|---|---|---|
network_definition_file |
String | Path to the network definition file (prototxt) | |
pretrained_model_file |
String | Path to the Pretrained model (weights) | |
points_src |
String | Input topic Pointcloud. Default. | /points_raw |
score_threshold |
Double | Minimum score required as given by the network to include the result (0.-1.) | 0.6 |
use_gpu |
Bool | Whether ot not to use a GPU device | true |
gpu_device_id |
Int | GPU ID | 0 |
width |
Int | Width of the 2d cluster | 512 |
height |
Int | Height of the 2d cluster | 512 |
range |
Int | Range for the 2d cluster | 60 |
use_constant_feature |
Bool | Use constant model feature (8 features) | false |
normalize_lidar_intensity |
Bool | Normalize the received lidar intensity data | false |
Outputs
Topic | Type | Description |
---|---|---|
/detection/lidar_detector/points_cluster |
sensor_msgs/PointCloud2 |
Colored PointCloud of the resulting detected objects |
/detection/lidar_detector/objects |
autoware_msgs/DetectedObjetArray |
Array of Detected Objects in Autoware format |
Notes
-
To display the results in Rviz
objects_visualizer
is required. (Launch file launches automatically this node). -
Pre trained models can be downloaded from the Apollo project repository.
Changelog for package lidar_apollo_cnn_seg_detect
1.11.0 (2019-03-21)
- [feature] Baidu's CNN based LiDAR segmentation
(#1800)
-
Add build caffe
-
add include apollo files
-
add apollo cnn
-
calculating time
-
- Cleaned node
-
Parametrized inputs
-
Works with custom caffe
-
- Parameterized
-
Works with custom Caffe
-
Removed hard coded params
-
Cleaned up dependencies
-
Added bboxes, labels
-
Minor fixes
-
Custom input topic
-
Added UI, launch file, readme
-
Added Compatibility for Perception Cleanup
-
- Added license messages
-
Updated readme
-
Added extra instructions
- Fix markdown
-
- Contributors: Abraham Monrroy Cano
Package Dependencies
Deps | Name |
---|---|
autoware_build_flags | |
catkin | |
autoware_msgs | |
geometry_msgs | |
pcl_ros | |
roscpp | |
sensor_msgs | |
tf | |
tf_conversions |
System Dependencies
Dependant Packages
Launch files
- launch/lidar_apollo_cnn_seg_detect.launch
-
- network_definition_file
- pretrained_model_file
- points_src [default: /points_raw]
- score_threshold [default: 0.6]
- use_gpu [default: true]
- gpu_device_id [default: 0]
- width [default: 512]
- height [default: 512]
- range [default: 60]
- use_constant_feature [default: false]
- normalize_lidar_intensity [default: false]
Messages
Services
Plugins
Recent questions tagged lidar_apollo_cnn_seg_detect at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 1.12.0 |
License | BSD |
Build type | CATKIN |
Use | RECOMMENDED |
Repository Summary
Description | autoware.ai perf |
Checkout URI | https://github.com/is-whale/autoware_learn.git |
VCS Type | git |
VCS Version | 1.14 |
Last Updated | 2025-03-14 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kosuke Murakami
Authors
- Kosuke Murakami
CNN LiDAR Baidu Object Segmenter
Autoware package based on Baidu’s object segmenter.
Pre requisites
Caffe distributable installed in your home (~/caffe/distribute
).
$ cd
$ git clone https://github.com/BVLC/caffe
$ cd caffe
Follow instructions from Installing Caffe from source.
- Use offical Make compilation procedure.
- Do not use thirdparty CMake setup.
Compile and create distributable:
$ make
$ make distribute
Recompile Autoware to build the node.
The Pretrained model
Use this link to download the pretrained model from Baidu:
https://github.com/ApolloAuto/apollo/tree/v5.5.0/modules/perception/production/data/perception/lidar/models/cnnseg
These two files are needed:
- deploy.prototxt
- deploy.caffemodel
How to launch
- From a sourced terminal:
Using rosrun:
rosrun lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect _network_definition_file:=/PATH/TO/FILE.prototxt _pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel _points_src:=/points_raw
Using launch file:
roslaunch lidar_apollo_cnn_seg_detect lidar_apollo_cnn_seg_detect.launch network_definition_file:=/PATH/TO/FILE.prototxt pretrained_model_file:=/PATH/TO/WEIGHTS.caffemodel points_src:=/points_raw
- From Runtime Manager:
Computing Tab -> Detection/ lidar_detector -> lidar_cnn_baidu_detect
. Configure parameters using the [app]
button.
Parameters
Parameter | Type | Description | Default |
---|---|---|---|
network_definition_file |
String | Path to the network definition file (prototxt) | |
pretrained_model_file |
String | Path to the Pretrained model (weights) | |
points_src |
String | Input topic Pointcloud. Default. | /points_raw |
score_threshold |
Double | Minimum score required as given by the network to include the result (0.-1.) | 0.6 |
use_gpu |
Bool | Whether ot not to use a GPU device | true |
gpu_device_id |
Int | GPU ID | 0 |
width |
Int | Width of the 2d cluster | 512 |
height |
Int | Height of the 2d cluster | 512 |
range |
Int | Range for the 2d cluster | 60 |
use_constant_feature |
Bool | Use constant model feature (8 features) | false |
normalize_lidar_intensity |
Bool | Normalize the received lidar intensity data | false |
Outputs
Topic | Type | Description |
---|---|---|
/detection/lidar_detector/points_cluster |
sensor_msgs/PointCloud2 |
Colored PointCloud of the resulting detected objects |
/detection/lidar_detector/objects |
autoware_msgs/DetectedObjetArray |
Array of Detected Objects in Autoware format |
Notes
-
To display the results in Rviz
objects_visualizer
is required. (Launch file launches automatically this node). -
Pre trained models can be downloaded from the Apollo project repository.
Changelog for package lidar_apollo_cnn_seg_detect
1.11.0 (2019-03-21)
- [feature] Baidu's CNN based LiDAR segmentation
(#1800)
-
Add build caffe
-
add include apollo files
-
add apollo cnn
-
calculating time
-
- Cleaned node
-
Parametrized inputs
-
Works with custom caffe
-
- Parameterized
-
Works with custom Caffe
-
Removed hard coded params
-
Cleaned up dependencies
-
Added bboxes, labels
-
Minor fixes
-
Custom input topic
-
Added UI, launch file, readme
-
Added Compatibility for Perception Cleanup
-
- Added license messages
-
Updated readme
-
Added extra instructions
- Fix markdown
-
- Contributors: Abraham Monrroy Cano
Package Dependencies
Deps | Name |
---|---|
autoware_build_flags | |
catkin | |
autoware_msgs | |
geometry_msgs | |
pcl_ros | |
roscpp | |
sensor_msgs | |
tf | |
tf_conversions |
System Dependencies
Dependant Packages
Launch files
- launch/lidar_apollo_cnn_seg_detect.launch
-
- network_definition_file
- pretrained_model_file
- points_src [default: /points_raw]
- score_threshold [default: 0.6]
- use_gpu [default: true]
- gpu_device_id [default: 0]
- width [default: 512]
- height [default: 512]
- range [default: 60]
- use_constant_feature [default: false]
- normalize_lidar_intensity [default: false]