The LiDAR segmenters library, for segmentation-based detection.
- We name your ros workspace as
CATKIN_WS
andgit 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
- Terminal 1: KiTTI raw dataset replay, more tutorials.
- 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.
- 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).
- Subscribe Point Cloud in topic
- 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.
- Get these paramters using
- PCL RANSAC, ICCV2011 PCL-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} }
- PCL Euclidean Cluster Extraction, ICCV2011 PCL-Segmentation
- 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} }