Fast and robust global registration for terrestrial robots @ ICRA2022
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-implmenation, so it is not exactly same with the original version.
include/quatro.hpp
). It requires other hpp files, though :sweat_smile:Registration
class in Point Cloud Library (PCL). Quatro can be used as follows:// After the declaration of Quatro,
quatro.setInputSource(srcMatched);
quatro.setInputTarget(tgtMatched);
Eigen::Matrix4d output;
quatro.computeTransformation(output);
Robust global registration performance
KITTI dataset | NAVER LABS Loc. dataset |
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[email protected]
[email protected]
[email protected]
The code is tested successfully at
sudo apt install cmake libeigen3-dev libboost-all-dev
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
.
In this study, 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, 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. 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:
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
~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
OMP_NUM_THREADS=4 roslaunch quatro quatro.launch
(Unfortunately, for the first run, it shows 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) |
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If our research has been helpful, please cite the below papers:
@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.},
year={2022},
pages={Accepted. To appear}
}
@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},
}
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, judgement 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).
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.