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https://github.com/url-kaist/quatro

Fast and robust global registration for terrestrial robots @ ICRA2022
https://github.com/url-kaist/quatro

global-registration lidar-point-cloud lidar-registration point-cloud-library point-cloud-registration registration

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Fast and robust global registration for terrestrial robots @ ICRA2022

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README 

        
# Quatro 



Official page of *"A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments"*, which is accepted @ ICRA'22. **NOTE that this repository is the re-implementation, so it is not exactly the same as the original version**.   



#### [[Video]](https://www.youtube.com/results?search_query=A+single+correpsondence+is+enough) [[Priprint Paper]](https://arxiv.org/abs/2203.06612)



## Demo



![](materials/README_demo_v2.gif)



## NEWS (May 21 2024)



Now, Quatro is fully supported by [TEASER++ library](https://github.com/MIT-SPARK/TEASER-plusplus). Those who want a ROS-free version should visit that site, please :)



## NEWS (Jan. 27 2023)

- An improved version is under review in the International Journal of Robotics Research~(IJRR)

- The codes will be refactored and then updated soon!



## Characteristics



* Single hpp file (`include/quatro.hpp`). It requires other hpp files, though :sweat_smile:

* Intuitive usage based on the hierarchical inheritance of the `Registration` class in [Point Cloud Library (PCL)](https://pointclouds.org/). Quatro can be used as follows: 



```c++

// After the declaration of Quatro,

quatro.setInputSource(srcMatched);

quatro.setInputTarget(tgtMatched);

Eigen::Matrix4d output;

quatro.computeTransformation(output);

```



* Robust global registration performance



  - As shown in the below figures, our method shows the most promising performance compared with other state-of-the-art methods.

  - It could be over-claim, yet our method would be more appropriate for terrestrial mobile platforms. Reducing the degree of freedom (DoF) usually makes algorithms be more robust against errors by projecting them into a lower dimension.

      - E.g. 3D-3D correspondences -> 3D-2D correspondences, LOAM -> LeGO-LOAM

  - In this context, our method is the dimension-reduced version of [TEASER++](https://github.com/MIT-SPARK/TEASER-plusplus)! 



KITTI dataset                  |  NAVER LABS Loc. dataset

:-------------------------:|:-------------------------:

![](materials/kitti_for_readme.PNG) |  ![](materials/labs_for_readme.PNG)



#### Contributors



* Beomsoo Kim (as a research intern): `[email protected]`

* Daebeom Kim (as a research intern): `[email protected]`

* Hyungtae Lim: `[email protected]`



#### ToDo



- [x] Add ROS support

- [x] Add demo videos

- [ ] Add preprint paper

- [ ] Add diverse examples for other sensor configuration



---



## Contents

1. [Test Env.](#Test-Env.)

0. [How to Build](#How-to-Build)

0. [How to Run Quatro](#How-to-Run-Quatro)

0. [Citation](#citation)



### Test Env.



The code is tested successfully at

* Linux 18.04 LTS

* ROS Melodic



## How to Build



### ROS Setting

1. Install the following dependencies



```

sudo apt install cmake libeigen3-dev libboost-all-dev

```



2. Install [ROS](http://torch.ch/docs/getting-started.html) on a machine.

3. Then, build Quatro package and enjoy! :) We use [catkin tools](https://catkin-tools.readthedocs.io/en/latest/) 



```

mkdir -p ~/catkin_ws/src

cd ~/catkin_ws/src

git clone [email protected]:url-kaist/Quatro.git

cd ~/catkin_ws

catkin build quatro 

```



**Note** Quatro requires `pmc` library, which is automatically installed via `3rdparty/find_dependencies.cmake`. 



## How to Run Quatro



### Prerequisites



In this study, the fast point feature histogram (FPFH) is utilized, which is widely used as a conventional descriptor for the registration. However, the original FPFH for a 3D point cloud captured by a 64-channel LiDAR sensor takes **tens of seconds**, which is too slow.  In summary, still, feature extraction & matching is the bottleneck for global registration :worried: (in fact, the accuracy is not very important because Quatro is extremely robust against outliers!).



For this reason, we employ voxel-sampled FPFH, which is preceded by voxel-sampling. This is followed by the correspondence test. In addition, we employ [Patchwork](https://arxiv.org/abs/2108.05560), which is the state-of-the-art method for ground segmentation, and image projection to reject some subclusters, which is proposed in [Le-GO-LOAM](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM). These modules are not presented in our paper! 



Finally, we can reduce the computational time of feature extraction & matching, i.e. the front-end of global registration, from tens of seconds to almost 0.2 sec. The overall pipeline is as follows:



![](materials/quatro_overview.PNG)



#### Note



For fine-tuning of the parameters to use this code in your own situations, please refer to `config` folder. In particular, for fine-tuning of Patchwork, please refer to [this Wiki](https://github.com/LimHyungTae/patchwork/wiki/4.-IMPORTANT:-Setting-Parameters-of-Patchwork-in-Your-Own-Env.)



### TL;DR

1. ~Download toy pcd bins files~ 

  

~The point clouds are from the KITTI dataset, so these are captured by Velodyne-64-HDE~



Toy pcds are automatically downloaded. If there is a problem, follow the below commands:



```

roscd quatro

cd materials

wget https://urserver.kaist.ac.kr/publicdata/quatro/000540.bin

wget https://urserver.kaist.ac.kr/publicdata/quatro/001319.bin

```



2. Launch the roslaunch file as follows:



```

OMP_NUM_THREADS=4 roslaunch quatro quatro.launch

```



(Unfortunately, for the first run, it shows a rather slow and imprecise performance. It may be due to multi-thread issues.)



Visualized inner pipelines        |  Source (red), target (green), and the estimated output (blue)

:-------------------------:|:-------------------------:

![](materials/quatro_inner.png) |  ![](materials/quatro_output.png)



## Citation



If our research has been helpful, please cite the below papers:



```

@article{lim2024quatro++,

  title={Quatro++: Robust global registration exploiting ground segmentation for loop closing in LiDAR SLAM},

  author={Lim, Hyungtae and Kim, Beomsoo and Kim, Daebeom and Mason Lee, Eungchang and Myung, Hyun},

  journal={The International Journal of Robotics Research},

  volume={43},

  number={5},

  pages={685--715},

  year={2024}

}

```



```

@article{lim2022quatro,

    title={A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments},

    author={Lim, Hyungtae and Yeon, Suyong and Ryu, Suyong and Lee, Yonghan and Kim, Youngji and Yun, Jaeseong and Jung, Euigon and Lee, Donghwan and Myung, Hyun},

    booktitle={Proc. IEEE Int. Conf. Robot. Autom.},

    pages={8010--8017},

    year={2022}

    }

```



```

@article{lim2021patchwork,

    title={Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor},

    author={Lim, Hyungtae and Minho, Oh and Myung, Hyun},

    journal={IEEE Robot. Autom. Lett.},

    volume={6},

    number={4},

    pages={6458--6465},

    year={2021},

    }

```



## Acknowledgment

This work was supported by the Industry Core Technology Development Project, 20005062, Development of Artificial Intelligence Robot Autonomous Navigation Technology for Agile Movement in Crowded Space, funded by the Ministry of Trade, Industry & Energy (MOTIE, Republic of Korea) and by the research project “Development of A.I. based recognition, judgment and control solution for autonomous vehicle corresponding to atypical driving environment,” which is financed from the Ministry of Science and ICT (Republic of Korea) Contract No. 2019-0-00399. The student was supported by the BK21 FOUR from the Ministry of Education (Republic of Korea).



## License

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.



### Copyright

- All codes on this page are copyrighted by KAIST published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. You must attribute the work in the manner specified by the author. You may not use the work for commercial purposes, and you may only distribute the resulting work under the same license if you alter, transform, or create the work.