# cloud_segmentation

**Repository Path**: li-tianping/cloud_segmentation

## Basic Information

- **Project Name**: cloud_segmentation
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-08
- **Last Updated**: 2022-01-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ROS package for point cloud segmentation
 
This package implements geometric segmentation of depth clouds based on region growing. First, the incoming clouds are filtered, normals are estimated for each 3D point, and then [PCL region growing segmentation](https://pcl.readthedocs.io/projects/tutorials/en/latest/region_growing_segmentation.html) is applied. 

The [rgbd_segmentation](https://github.com/ethz-asl/rgbd_segmentation) package combines the geometric segmentation implemented here with instance-aware semantic segmentation via [Mask R-CNN](https://github.com/ethz-asl/mask_rcnn_ros) to obtain combined geometric-semantic segmentation results.
 
This package was developed as part of the [**TSDF++** framework](https://github.com/ethz-asl/tsdf-plusplus) for multiple dynamic object tracking and reconstruction. 

## Citing 

When using this code in your research, please cite the following publication:

Margarita Grinvald, Federico Tombari, Roland Siegwart, and Juan Nieto, **TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction**, in _2021 IEEE International Conference on Robotics and Automation (ICRA)_, 2021. [[Video](https://youtu.be/dSJmoeVasI0)]

```bibtex
@INPROCEEDINGS{grinvald2021tsdf,
  author={Grinvald, Margarita and Tombari, Federico and Siegwart, Roland and Nieto, Juan},
  booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={{TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction}}, 
  year={2021},
  volume={},
  number={},
  pages={14192-14198},
  doi={10.1109/ICRA48506.2021.9560923}}
```


## Installation

The installation has been tested on Ubuntu 16.04 and Ubutnu 20.04.

### Requirements
- ROS 
- C++14 for [PCL 1.10](https://github.com/PointCloudLibrary/pcl)

### Install dependencies
Install ROS following the instructions at the [ROS installation page](http://wiki.ros.org/ROS/Installation). The full install (`ros-kinetic-desktop-full`, `ros-melodic-desktop-full`) are recommended. 

Make sure to source your ROS _setup.bash_ script by following the instructions on the ROS installation page.

### Installation on Ubuntu
In your terminal, define the installed ROS version and name of the catkin workspace to use:
```bash
export ROS_VERSION=kinetic # (Ubuntu 16.04: kinetic, Ubuntu 18.04: melodic)
export CATKIN_WS=~/catkin_ws
```

If you don't have a [catkin](http://wiki.ros.org/catkin) workspace yet, create a new one:
```bash
mkdir -p $CATKIN_WS/src && cd $CATKIN_WS
catkin init
catkin config --extend /opt/ros/$ROS_VERSION --merge-devel 
catkin config --cmake-args -DCMAKE_CXX_STANDARD=14 -DCMAKE_BUILD_TYPE=Release
wstool init src
```

Clone the `cloud_segmentation` repository over HTTPS (no Github account required) and automatically fetch dependencies:
```bash
cd $CATKIN_WS/src
git clone https://github.com/ethz-asl/cloud_segmentation.git
wstool merge -t . cloud_segmentation/cloud_segmentation_https.rosinstall
wstool update
```

Alternatively, clone over SSH (Github account required):
```bash
cd $CATKIN_WS/src
git clone git@github.com:ethz-asl/cloud_segmentation.git
wstool merge -t . cloud_segmentation/cloud_segmentation_ssh.rosinstall
wstool update
```

Build and source the fetched packages:
```bash
catkin build
source ../devel/setup.bash # (bash shell: ../devel/setup.bash,  zsh shell: ../devel/setup.zsh)
```

## License
The code is available under the [MIT license](https://github.com/ethz-asl/cloud_segmentation/blob/master/LICENSE).