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lidar_apollo_instance_segmentation#

Peek 2020-04-07 00-17

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

Supported lidars are velodyne 16, 64 and 128, but you can also use velodyne 32 and other lidars with good accuracy.

  1. apollo 3D Obstacle Perception description

    /******************************************************************************
* 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.
*****************************************************************************/

  2. tensorRTWrapper : It is used under the lib directory.

    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
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

  3. autoware_perception description

    /*
* Copyright 2018-2019 Autoware Foundation. 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.
*/

Special thanks#

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