autoware_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]
build_only	bool	false	shutdown the node after TensorRT engine file is built

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.

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

• Apollo Project
• lewes6369
• Autoware Foundation
• Kosuke Takeuchi (TIER IV)