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https://github.com/izhengfan/se2lam
(ICRA 2019) Visual-Odometric On-SE(2) Localization and Mapping
https://github.com/izhengfan/se2lam
g2o graph-optimization icra icra2019 robotics ros slam state-estimation visual-slam
Last synced: about 1 month ago
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(ICRA 2019) Visual-Odometric On-SE(2) Localization and Mapping
- Host: GitHub
- URL: https://github.com/izhengfan/se2lam
- Owner: izhengfan
- License: mit
- Created: 2019-03-04T08:05:07.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-11-21T03:19:03.000Z (over 3 years ago)
- Last Synced: 2024-01-11T03:33:03.528Z (5 months ago)
- Topics: g2o, graph-optimization, icra, icra2019, robotics, ros, slam, state-estimation, visual-slam
- Language: C++
- Homepage: https://github.com/izhengfan/se2lam
- Size: 1.17 MB
- Stars: 396
- Watchers: 16
- Forks: 108
- Open Issues: 5
-
Metadata Files:
- Readme: README.MD
- License: LICENSE
Lists
- awesome-robotic-tooling - se2lam - On-SE(2) Localization and Mapping for Ground Vehicles by Fusing Odometry and Vision. (Localization and State Estimation / Lidar and Point Cloud Processing)
- Awesome-SLAM - se2lam - Odometric On-SE(2) Localization and Mapping (2. Visual SLAM / 2.5 Others)
- awesome-robotic-tooling - se2lam - On-SE(2) Localization and Mapping for Ground Vehicles by Fusing Odometry and Vision. (Localization and State Estimation / Lidar and Point Cloud Processing)
README
se2lam
---
On-SE(2) Localization and Mapping for Ground Vehicles by Fusing Odometry and Vision
[](../../actions?query=workflow%3A"Build")
### Related Publication
- Fan Zheng, Yun-Hui Liu. "Visual-Odometric Localization and Mapping for Ground Vehicles Using SE(2)-XYZ Constraints". _Proc. IEEE International Conference on Robotics and Automation (ICRA)_, 2019 \[[pdf](https://fzheng.me/icra/2019.pdf)\] \[[poster](poster_fzheng.pdf)\]
To cite it in bib:
```
@inproceedings{fzheng2019icra,
author = {Fan Zheng and Yun-Hui Liu},
title = "{Visual-Odometric Localization and Mapping for Ground Vehicles Using SE(2)-XYZ Constraints}",
booktitle = {Proc. IEEE Int. Conf. Robot. Autom (ICRA)},
year = {2019},
}
```
[](https://mycuhk-my.sharepoint.com/:v:/g/personal/1155051778_link_cuhk_edu_hk/EeIO3MJtH5pHsFkIRGHJbLEBRhRBGRRG6pwR19SFCrhQwQ?e=vbSLzS)
### Dependencies
- ROS (tested on Kinetic/Melodic)
- OpenCV 2.4.x / 3.1 above
- g2o ([2016 version](https://github.com/RainerKuemmerle/g2o/releases/tag/20160424_git))
### Build
Build this project as a ROS package
### Demo
1. Download [DatasetRoom.zip](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155051778_link_cuhk_edu_hk/Ef4NuXvLZI1JhfljH9LkNxUB5xrDrCOrRnxwztO5bGKlew?e=U4aind), and extract it. In a terminal, `cd` into `DatasetRoom/`.
We prepare two packages of odometry measurement data, one is more accurate (`odo_raw_accu.txt`), the other less accurate (`odo_raw_roug.txt`). To use either one of them, copy it to `odo_raw.txt` in `DatasetRoom/`.
2. Download [ORBvoc.bin](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155051778_link_cuhk_edu_hk/EaF2ZkP17rdJrUHT0mrcf74Bl1h_691xZrxNILGbQbYFmA?e=nXRSS4).
3. Run rviz:
```
roscd se2lam
rosrun rviz rviz -d rviz.rviz
```
4. Run se2lam:
```
rosrun se2lam test_vn PATH_TO_DatasetRoom PATH_TO_ORBvoc.bin
```
### Related Project
[izhengfan/se2clam](https://github.com/izhengfan/se2clam)
[izhengfan/ORB_SLAM2](https://github.com/izhengfan/ORB_SLAM2)
### License
[MIT](LICENSE)