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https://github.com/PRBonn/depth_clustering

:taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor.
https://github.com/PRBonn/depth_clustering

catkin clustering depth depth-clustering depth-image fast lidar pcl point-cloud range range-image real-time robotics ros segmentation velodyne velodyne-sensor

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:taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor.

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README 

        
# 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:

[![Segmentation illustration](doc/pics/depth_clustering_new_short_1.gif)](https://www.youtube.com/watch?v=UXHX9kFGXfg "Segmentation")



## Prerequisites ##

I recommend using a virtual environment in your catkin workspace (``

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             # 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):



Ubuntu 14.04



#### Install these packages:



```bash

sudo apt install libopencv-dev libqglviewer-dev freeglut3-dev libqt4-dev

```



Ubuntu 16.04



#### Install these packages:



```bash

sudo apt install libopencv-dev libqglviewer-dev freeglut3-dev libqt5-dev

```



Ubuntu 18.04



#### 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.



### 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 /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 OpenFolder button and choose the

folder where you have unpacked the `png` files, e.g. `data/scenario1/`.

Navigate the viewer with arrows and controls seen on screen.



#### Other data ####

There are also examples on how to run the processing on KITTI data and on ROS

input. Follow the `--help` output of each of the examples for more details.



Also you can load the data from the GUI. Make sure you are loading files with

correct extension (`*.txt` and `*.bin` for KITTI, `*.png` for Moosmann's data).



## Documentation ##

You should be able to get Doxygen documentation by running:

```

cd doc/

doxygen Doxyfile.conf

```



## Related publications ##

Please cite related papers if you use this code:



```

@InProceedings{bogoslavskyi16iros,

title     = {Fast Range Image-Based Segmentation of Sparse 3D Laser Scans for Online Operation},

author    = {I. Bogoslavskyi and C. Stachniss},

booktitle = {Proc. of The International Conference on Intelligent Robots and Systems (IROS)},

year      = {2016},

url       = {http://www.ipb.uni-bonn.de/pdfs/bogoslavskyi16iros.pdf}

}

```



```

@Article{bogoslavskyi17pfg,

title   = {Efficient Online Segmentation for Sparse 3D Laser Scans},

author  = {I. Bogoslavskyi and C. Stachniss},

journal = {PFG -- Journal of Photogrammetry, Remote Sensing and Geoinformation Science},

year    = {2017},

pages   = {1--12},

url     = {https://link.springer.com/article/10.1007%2Fs41064-016-0003-y},

}

```



[travis-img]: https://img.shields.io/travis/PRBonn/depth_clustering/master.svg?style=for-the-badge

[travis-link]: https://travis-ci.org/PRBonn/depth_clustering



[coveralls-img]: https://img.shields.io/coveralls/github/PRBonn/depth_clustering/master.svg?style=for-the-badge

[coveralls-link]: https://coveralls.io/github/PRBonn/depth_clustering



[build-status-img]: https://gitlab.ipb.uni-bonn.de/igor/depth_clustering/badges/master/build.svg

[coverage-img]: https://gitlab.ipb.uni-bonn.de/igor/depth_clustering/badges/master/coverage.svg

[commits-link]: https://gitlab.ipb.uni-bonn.de/igor/depth_clustering/commits/master



[catkin_tools_docs]: https://catkin-tools.readthedocs.io/en/latest/installing.html