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 2 months 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 (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2024-06-04T09:01:55.000Z (7 months ago)
• Last Synced: 2024-08-02T07:06:57.575Z (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: 1.15 MB
• Stars: 403
• Watchers: 17
• Forks: 108
• Open Issues: 3
• Metadata Files:
  • Readme: README.MD
  • License: LICENSE

Awesome Lists containing this project

• 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

[![](../../workflows/Build/badge.svg)](../../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},

  }

  ```

  [![result in rviz](https://images.gitee.com/uploads/images/2019/0304/152353_36314cbb_874043.jpeg)](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)