segmenters_lib

The LiDAR segmenters library, for segmentation-based detection.

How to use

We name your ros workspace as CATKIN_WS and git clone as a ROS package, with common_lib and object_builders_lib as dependencies.

$ cd $(CATKIN_WS)
# we recommand you to organize your workspace as following
$ mkdir -p src/common
$ mkdir -p src/perception/libs

# git clone basic libraries, like common_lib
$ cd $(CATKIN_WS)/src/common
$ git clone https://github.com/LidarPerception/common_lib.git libs

# git clone perception libraries, segmenters_lib and its dependencies
$ cd $(CATKIN_WS)/src/perception/libs
$ git clone https://github.com/LidarPerception/roi_filters_lib.git roi_filters
$ git clone https://github.com/LidarPerception/object_builders_lib.git object_builders
$ git clone https://github.com/LidarPerception/segmenters_lib.git segmenters

# build your ros workspace for our segmentation-based detection demo
# uncomment add_subdirectory() in src/perception/libs/segmenters/CMakeLists.txt, git diff as following:
-#add_subdirectory(example)
+add_subdirectory(example)
$ cd $(CATKIN_WS)
$ catkin build -DCMAKE_BUILD_TYPE=Release

Run demo under KiTTI raw dataset using kitti_ros's replayer.

$ cd $(CATKIN_WS)/src
$ git clone https://github.com/LidarPerception/kitti_ros.git
# build your ros workspace for our segmentation-based detection demo
$ cd ..
$ catkin build -DCMAKE_BUILD_TYPE=Release

Terminal 1: KiTTI raw dataset replay, more tutorials.

$ cd $(CATKIN_WS)
$ source devel/setup.bash
# change Mode for Keyboard Listening Device
$ sudo chmod 777 /dev/input/event3
# launch kitti_ros's kitti_player for frame-by-frame algorithm testing
$ roslaunch kitti_ros kitti_player.launch

Terminal 2: launch Seg-based Detector demo.

$ cd $(CATKIN_WS)
$ source devel/setup.bash
$ roslaunch segmenters_lib demo.launch

Follow the demo example to use our LiDAR segmenters library.
- Cascadingly use roi_filter, ground_remover and non_ground_segmenter for Point Cloud perception, like our Seg-based Detector: detection_node.
- Refer to our CMakeLists.txt for building your own ros package using this library.

Parameters

Demo parameters, defined in detection.yaml
- Subscribe Point Cloud in topic sub_pc_topic, default is /kitti/points_raw (sensor_msgs/PointCloud2).
- Publish Ground Point Cloud in topic pub_pc_ground_topic, default is /segmenter/points_ground (sensor_msgs/PointCloud2).
- Publish Non-Ground Point Cloud in topic pub_pc_nonground_topic, default is /segmenter/points_nonground (sensor_msgs/PointCloud2).
- Publish Candidate Objects Cloud, different objects with different intensity, in topic pub_pc_clusters_topic, default is /segmenter/points_clustered (sensor_msgs/PointCloud2).
Segmenters algorithm parameters, defined in segmenter.yaml.
- Get these paramters using common::getSegmenterParams(const ros::NodeHandle& nh, const std::string& ns_prefix) in common_lib.
- Adjust some paramters to your hardware installation.

TODO lists

Ground Segmenters

PCL RANSAC, ICCV2011 PCL-Segmentation

Refer: PCL: Plane model segmentation

GPF (Ground Plane Fitting), ICRA 2017

@inproceedings{zermas2017fast,
  title={Fast segmentation of 3d point clouds: A paradigm on lidar data for autonomous vehicle applications},
  author={Zermas, Dimitris and Izzat, Izzat and Papanikolopoulos, Nikolaos},
  booktitle={Robotics and Automation (ICRA), 2017 IEEE International Conference on},
  pages={5067--5073},
  year={2017},
  organization={IEEE}
}

linefit_ground_segmentation, IV 2010

@inproceedings{himmelsbach2010fast,
  title={Fast segmentation of 3d point clouds for ground vehicles},
  author={Himmelsbach, Michael and Hundelshausen, Felix V and Wuensche, H-J},
  booktitle={Intelligent Vehicles Symposium (IV), 2010 IEEE},
  pages={560--565},
  year={2010},
  organization={IEEE}
}

depth_clustering, IROS 2016

@inproceedings{bogoslavskyi2016fast,
  title={Fast range image-based segmentation of sparse 3D laser scans for online operation},
  author={Bogoslavskyi, Igor and Stachniss, Cyrill},
  booktitle={Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on},
  pages={163--169},
  year={2016},
  organization={IEEE}
}

Scan Line Run, ICRA 2017, like Standford's Junior self-driving car.

@inproceedings{zermas2017fast,
  title={Fast segmentation of 3d point clouds: A paradigm on lidar data for autonomous vehicle applications},
  author={Zermas, Dimitris and Izzat, Izzat and Papanikolopoulos, Nikolaos},
  booktitle={Robotics and Automation (ICRA), 2017 IEEE International Conference on},
  pages={5067--5073},
  year={2017},
  organization={IEEE}
}
@incollection{montemerlo2009junior,
  title={Junior: The stanford entry in the urban challenge},
  author={Montemerlo, Michael and Becker, Jan and Bhat, Suhrid and Dahlkamp, Hendrik and Dolgov, Dmitri and Ettinger, Scott and Haehnel, Dirk and Hilden, Tim and Hoffmann, Gabe and Huhnke, Burkhard and others},
  booktitle={The DARPA Urban Challenge},
  pages={91--123},
  year={2009},
  publisher={Springer}
}

Deep-learning: FCN, IV 2017

@inproceedings{caltagirone2017fast,
  title={Fast LIDAR-based road detection using fully convolutional neural networks},
  author={Caltagirone, Luca and Scheidegger, Samuel and Svensson, Lennart and Wahde, Mattias},
  booktitle={Intelligent Vehicles Symposium (IV), 2017 IEEE},
  pages={1019--1024},
  year={2017},
  organization={IEEE}
}

Non-ground Segmenters

PCL Euclidean Cluster Extraction, ICCV2011 PCL-Segmentation

Refer: PCL: Euclidean Cluster Extraction

Region-based Euclidean Cluster Extraction

@inproceedings{yan2017online,
  title={Online learning for human classification in 3d lidar-based tracking},
  author={Yan, Zhi and Duckett, Tom and Bellotto, Nicola},
  booktitle={Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on},
  pages={864--871},
  year={2017},
  organization={IEEE}
}

Model-based Segmentation, ITSC 2017

@article{shin2017real,
  title={Real-time and accurate segmentation of 3-D point clouds based on Gaussian process regression},
  author={Shin, Myung-Ok and Oh, Gyu-Min and Kim, Seong-Woo and Seo, Seung-Woo},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  volume={18},
  number={12},
  pages={3363--3377},
  year={2017},
  publisher={IEEE}
}

Probabilistic Framework, RSS 2016

@inproceedings{held2016probabilistic,
  title={A Probabilistic Framework for Real-time 3D Segmentation using Spatial, Temporal, and Semantic Cues.},
  author={Held, David and Guillory, Devin and Rebsamen, Brice and Thrun, Sebastian and Savarese, Silvio},
  booktitle={Robotics: Science and Systems},
  year={2016}
}

Tracking-help Segmentation. IV, 2012. Implemented in our tracking_lib.

@inproceedings{himmelsbach2012tracking,
  title={Tracking and classification of arbitrary objects with bottom-up/top-down detection},
  author={Himmelsbach, Michael and Wuensche, H-J},
  booktitle={Intelligent Vehicles Symposium (IV), 2012 IEEE},
  pages={577--582},
  year={2012},
  organization={IEEE}
}