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:taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor.
| Date | Stars |
|---|---|
| 2026-07-24 | 1311 |
| 2026-07-25 | 1311 |
| 2026-07-28 | 1311 |
| 2026-07-30 | 1311 |
| 2026-08-06 | 1311 |
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# Depth Clustering # [![Build Status][travis-img]][travis-link] [![Coverage Status][coveralls-img]][coveralls-link] This is a fast and robust algorithm to segment point clouds taken with Velodyne sensor into objects. It works with all available Velodyne sensors, i.e. 16, 32 and 64 beam ones. Check out a video that shows all objects outlined in orange: [](https://www.youtube.com/watch?v=UXHX9kFGXfg "Segmentation") ## Prerequisites ## I recommend using a virtual environment in your catkin workspace (`<catkin_ws>` in this readme) and will assume that you have it set up throughout this readme. Please update your commands accordingly if needed. I will be using `pipenv` that you can install with `pip`. ### Set up workspace and catkin ### Regardless of your system you will need to do the following steps: ```bash cd <catkin_ws> # navigate to the workspace pipenv shell --fancy # start a virtual environment pip install catkin-tools # install catkin-tools for building mkdir src # create src dir if you don't have it already # Now you just need to clone the repo: git clone https://github.com/PRBonn/depth_clustering src/depth_clustering ``` ### System requirements ### You will need OpenCV, QGLViewer, FreeGLUT, QT4 or QT5 and optionally PCL and/or ROS. The following sections contain an installation command for various Ubuntu systems (click folds to expand): <details> <summary>Ubuntu 14.04</summary> #### Install these packages: ```bash sudo apt install libopencv-dev libqglviewer-dev freeglut3-dev libqt4-dev ``` </details> <details> <summary>Ubuntu 16.04</summary> #### Install these packages: ```bash sudo apt install libopencv-dev libqglviewer-dev freeglut3-dev libqt5-dev ``` </details> <details> <summary>Ubuntu 18.04</summary> #### Install these packages: ```bash sudo apt install libopencv-dev libqglviewer-dev-qt5 freeglut3-dev qtbase5-dev ``` You might also need the latest GoogleTest binary installed on your systems. As Ubuntu is not shipped with these binaries by default, you would have to install them yourself or adapt the build script to build them from source. </details> ### Optional requirements ### If you want to use PCL clouds and/or use ROS for data acquisition you can install the following: - (optional) PCL - needed for saving clouds to disk - (optional) ROS - needed for subscribing to topics ## How to build? ## This is a catkin package. So we assume that the code is in a catkin workspace and CMake knows about the existence of Catkin. It should be already taken care of if you followed the instructions [here](#set-up-workspace-and-catkin). Then you can build it from the project folder: ```bash mkdir build cd build cmake .. make -j4 ctest -VV # run unit tests, optional ``` It can also be built with `catkin_tools` if the code is inside catkin workspace: ```bash catkin build depth_clustering ``` P.S. in case you don't use `catkin build` you [should][catkin_tools_docs] reconsider your decision. ## How to run? ## See [examples](examples/). There are ROS nodes as well as standalone binaries. Examples include showing axis oriented bounding boxes around found objects (these start with `show_objects_` prefix) as well as a node to save all segments to disk. The examples should be easy to tweak for your needs. ## Run on real world data ## Go to folder with binaries: ``` cd <path_to_project>/build/devel/lib/depth_clustering ``` #### Frank Moosmann's "Velodyne SLAM" Dataset #### Get the data: ``` mkdir data/; wget http://www.mrt.kit.edu/z/publ/download/velodyneslam/data/scenario1.zip -O data/moosmann.zip; unzip data/moosmann.zip -d data/; rm data/moosmann.zip ``` Run a binary to show detected objects: ``` ./show_objects_moosmann --path data/scenario1/ ``` Alternatively, you can run the data from Qt GUI (as in video): ``` ./qt_gui_app ``` Once the GUI is shown, click on <kbd>OpenFolder</kbd> button and choose the
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matched fp:56a203e4b9bc2a93, topic:robotics, topic:ros, readme:slam