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All Projects → izhengfan → Se2lam

izhengfan / Se2lam

Licence: mit
(ICRA 2019) Visual-Odometric On-SE(2) Localization and Mapping

Labels

roboticsrosslam

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se2lam

On-SE(2) Localization and Mapping for Ground Vehicles by Fusing Odometry and Vision

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] [poster]

    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

Dependencies

  • ROS (tested on Kinetic/Melodic)

  • OpenCV 2.4.x / 3.1 above

  • g2o (2016 version)

Build

Build this project as a ROS package

Demo

  1. Download DatasetRoom.zip, 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.

  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
izhengfan/ORB_SLAM2

License

MIT

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