# semantic_suma

**Repository Path**: sdkmsdn_admin/semantic_suma

## Basic Information

- **Project Name**: semantic_suma
- **Description**: Semantic Mapping using Surfel Mapping and Semantic Segmentation (Chen et al IROS 2019)
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-27
- **Last Updated**: 2023-04-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SuMa++: Efficient LiDAR-based Semantic SLAM

This repository contains the implementation of SuMa++, which generates semantic maps only using three-dimensional laser range scans.

Developed by [Xieyuanli Chen](http://www.ipb.uni-bonn.de/people/xieyuanli-chen/) and [Jens Behley](http://jbehley.github.io/projects/surfel_mapping/).

SuMa++ is built upon SuMa and RangeNet++. For more details, we refer to the original project websites [SuMa](https://github.com/jbehley/SuMa) and [RangeNet++](https://github.com/PRBonn/lidar-bonnetal).

_An example of using SuMa++:_
![ptcl](./.readme/demo.gif)

## Publication

If you use our implementation in your academic work, please cite the corresponding [paper](http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2019iros.pdf):  
    
	@inproceedings{chen2019iros, 
			author = {X. Chen and A. Milioto and E. Palazzolo and P. Giguère and J. Behley and C. Stachniss},
			title  = {{SuMa++: Efficient LiDAR-based Semantic SLAM}},
			booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
			year = {2019},
			codeurl = {https://github.com/PRBonn/semantic_suma/},
			videourl = {https://youtu.be/uo3ZuLuFAzk},
	}


##  Dependencies

* catkin
* Qt5 >= 5.2.1
* OpenGL >= 4.0
* libEigen >= 3.2
* gtsam >= 4.0

In Ubuntu 16.04: Installing all dependencies should be accomplished by
```bash
sudo apt-get install build-essential cmake libgtest-dev libeigen3-dev libboost-all-dev qtbase5-dev libglew-dev libqt5libqgtk2 catkin
```

Additionally, make sure you have [catkin-tools](https://catkin-tools.readthedocs.io/en/latest/) and the [fetch](https://github.com/Photogrammetry-Robotics-Bonn/catkin_tools_fetch) verb installed:
```bash
sudo apt install python-pip
sudo pip install catkin_tools catkin_tools_fetch empy
```

## Build
#### rangenet_lib
To use SuMa++, you need to first build the rangenet_lib with the TensorRT and C++ interface. 
For more details about building and using rangenet_lib you could find in [rangenet_lib](https://github.com/PRBonn/rangenet_lib).

#### SuMa++
Clone the repository in the `src` directory of the same catkin workspace where you built the rangenet_lib:
```bash
git clone https://github.com/PRBonn/semantic_suma.git
```
Download the additional dependencies (or clone [glow](https://github.com/jbehley/glow.git) into your catkin workspace `src` yourself):
```bash
catkin deps fetch
```

For the first setup of your workspace containing this project, you need:
  ```bash
catkin build --save-config -i --cmake-args -DCMAKE_BUILD_TYPE=Release -DOPENGL_VERSION=430 -DENABLE_NVIDIA_EXT=YES
  ```
  
  Where you have to set `OPENGL_VERSION` to the supported OpenGL core profile version of your system, which you can query as follows:

```bash
$ glxinfo | grep "version"
server glx version string: 1.4
client glx version string: 1.4
GLX version: 1.4
OpenGL core profile version string: 4.3.0 NVIDIA 367.44
OpenGL core profile shading language version string: 4.30 NVIDIA [...]
OpenGL version string: 4.5.0 NVIDIA 367.44
OpenGL shading language version string: 4.50 NVIDIA
```

 Here the line `OpenGL core profile version string: 4.3.0 NVIDIA 367.44` is important and therefore you should use `-DOPENGL_VERSION = 430`. If you are unsure you can also leave it on the default version `330`, which should be supported by all OpenGL-capable devices.

 If you have a NVIDIA device, like a Geforce or Quadro graphics card, you should also activate the NVIDIA extensions using `-DENABLE_NVIDIA_EXT=YES` for info about the current GPU memory usage of the program.

 After this setup steps, you can build with `catkin build`, since the configuration has been saved to your current Catkin profile (therefore, `--save-config` was needed).
 
 Now the project root directory (e.g. `~/catkin_ws/src/semantic_suma`) should contain a `bin` directory containing the visualizer.

## How to run and use it?
**Important Notice**
- Before running SuMa++, you need to first build the [rangenet_lib](https://github.com/PRBonn/rangenet_lib) and download the pretrained [model](http://www.ipb.uni-bonn.de/html/projects/semantic_suma/darknet53.tar.gz).
- You need to specify the model path in the configuration file in the `config/` folder.
- For the first time using, rangenet_lib will take several minutes to build a `.trt` model for SuMa++.
- SuMa++ now can only work with KITTI dataset.

All binaries are copied to the `bin` directory of the source folder of the project. Thus,
1. run `visualizer` in the `bin` directory by `./visualizer`,
2. open a Velodyne directory from the KITTI Visual Odometry Benchmark and select a ".bin" file,
3. start the processing of the scans via the "play button" in the GUI.


## License


Copyright 2019, Xieyuanli Chen, Jens Behley, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.

This project is free software made available under the MIT License. For details see the LICENSE file.