AirDOS: Dynamic SLAM benefits from Articulated Objects

TartanAir Shibuya Dataset

You may download the dataset from: https://github.com/haleqiu/tartanair-shibuya

Instruction

Dependencies

• Eigen (Tested on v3.3)
• OpenCV (Tested on Opencv 3.4)
• Pangolin (v0.5)

Build

Run the ./build.sh in root directory to build third party libraries, AirDOS and construct a binary representation of ORB vocabulary file.

Run an example script

Currently, the project only supports Stereo mode. The stereo_kitti_human executable in Examples/Stereo

./Examples/Stereo/stereo_kitti_human \
  ./Vocabulary/ORBvoc.txt \
  ./Examples/Stereo/config/tartanair.yaml \
  /.../TartanAir_shibuya/RoadCrossing07 \
  ./Evaluation/data/trajectory_output.txt

Evaluate Trajectory

To plot the trajectories (in xy, yz, xz views), run ./Evaluation/Plot.py

For evaluation, please check https://github.com/castacks/tartanair_tools.git.

Note: the TartanAir Tools expect to read the trajectory format with 7 columns, representing the translation and quaternion.

The trajectory exported by the program contains 8 columns, where first column is the timestamp of pose.

To evaluate the exported trajectory using TartanAir Tools, you need to remove the first column of exported trajectory file.

Publications

AirDOS: Dynamic SLAM benefits from Articulated Objects

@inproceedings{qiu2022airdos,
  author={Qiu, Yuheng and Wang, Chen and Wang, Wenshan and Henein, Mina and Scherer, Sebastian},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={AirDOS: Dynamic SLAM benefits from Articulated Objects}, 
  year={2022},
  pages={8047-8053},
  doi={10.1109/ICRA46639.2022.9811667}
 }

Contributors

haleqiu, MarkChenYutian