# segmenters_lib **Repository Path**: ryontang/segmenters_lib ## Basic Information - **Project Name**: segmenters_lib - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2020-02-12 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # segmenters_lib [![CircleCI](https://circleci.com/gh/LidarPerception/segmenters_lib/tree/master.svg?style=svg)](https://circleci.com/gh/LidarPerception/segmenters_lib/tree/master) 　　The LiDAR segmenters library, for segmentation-based detection.

## How to use 1. We name your ros workspace as `CATKIN_WS` and `git clone` as a ROS package, with [common_lib](https://github.com/LidarPerception/common_lib) and [object_builders_lib](https://github.com/LidarPerception/object_builders_lib) as dependencies. ```bash $ 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 ``` 2. Run demo under [KiTTI raw dataset](http://www.cvlibs.net/datasets/kitti/raw_data.php) using [kitti_ros](https://github.com/LidarPerception/kitti_ros.git)'s replayer. ```bash $ 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](https://github.com/LidarPerception/kitti_ros#how-to-use). ```bash $ 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. ```bash $ cd $(CATKIN_WS) $ source devel/setup.bash $ roslaunch segmenters_lib demo.launch ``` 3. Follow the [demo example](./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](./example/detection_node.cpp). + Refer to our [CMakeLists.txt](./example/CMakeLists.txt) for building your own ros package using this library.

## [Parameters](./launch/demo.launch) + Demo parameters, defined in [detection.yaml](./config/kitti/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](./config/kitti/segmenter.yaml). + Get these paramters using `common::getSegmenterParams(const ros::NodeHandle& nh, const std::string& ns_prefix)` in [common_lib](https://github.com/LidarPerception/common_lib/blob/master/include/common/parameter.hpp). + Adjust some paramters to your hardware installation. ## TODO lists ### Ground Segmenters - [x] PCL RANSAC, [ICCV2011 PCL-Segmentation](./doc/ICCV2011%20PCL-Segmentation.pdf) > Refer: [PCL: Plane model segmentation](http://pointclouds.org/documentation/tutorials/planar_segmentation.php#planar-segmentation) - [x] GPF (Ground Plane Fitting), ICRA 2017 ```bibtex @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](https://github.com/lorenwel/linefit_ground_segmentation), IV 2010 ```bibtex @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](https://github.com/PRBonn/depth_clustering), IROS 2016 ```bintex @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. ```bibtex @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 ```bibtex @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 - [x] PCL Euclidean Cluster Extraction, [ICCV2011 PCL-Segmentation](./doc/ICCV2011%20PCL-Segmentation.pdf) > Refer: [PCL: Euclidean Cluster Extraction](http://pointclouds.org/documentation/tutorials/cluster_extraction.php#cluster-extraction) - [x] Region-based Euclidean Cluster Extraction ```bibtex @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 ```latex @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 ```bibtex @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} } ```

- [x] **Tracking-help Segmentation**. IV, 2012. Not yet in this repository. ```bibtex @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} } ```