SE(2)-Constrained Localization and Mapping by Fusing Odometry and Vision

• Fan Zheng, Hengbo Tang, Yun-Hui Liu. "Odometry-Vision-Based Ground Vehicle Motion Estimation With SE(2)-Constrained SE(3) Poses". IEEE Transactions on Cybernetics, 2018

  To cite it in bib:

  @article{fzheng2018tcyb,
    author  = {Fan Zheng and Hengbo Tang and Yun-Hui Liu},
    journal = {IEEE Trans. Cybernetics},
    title   = "{Odometry-Vision-Based Ground Vehicle Motion Estimation With SE(2)-Constrained SE(3) Poses}",
    year    = {2018},
  }
  

• ROS (tested on Kinetic/Melodic)

• OpenCV 2.4.x / 3.1 above

• g2o (2016 version)

Build this project as a ROS package

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 se2clam
   rosrun rviz rviz -d rviz.rviz
   

4. Run se2clam:

   rosrun se2clam test_vn PATH_TO_DatasetRoom PATH_TO_ORBvoc.bin
   

   

izhengfan/se2lam

MIT