/kaistviodataset

"Visual-Inertial Dataset" (RA-L'21 with ICRA'21): it contains harsh motions for VO/VIO, like pure rotation or fast rotation with various motion types.

Primary LanguageShellGNU General Public License v3.0GPL-3.0

KAIST VIO dataset (RA-L'21 w/ ICRA Option)

Official page of "Run Your Visual-Inertial Odometry on NVIDIA Jetson: Benchmark Tests on a Micro Aerial Vehicle", which is accepted by RA-L with ICRA'21 option

video
arxiv journal

This is the dataset for testing the robustness of various VO/VIO methods, acquired on a UAV.

You can download the whole dataset on KAIST VIO dataset


Contents

  1. Trajectories
  2. Downloads
  3. Dataset format
  4. Setup
  5. Citation

Trajectories


  • Four different trajectories:
    • circle
    • infinity
    • square
    • pure_rotation
  • Each trajectory has three types of sequence:
    • normal speed
    • fast speed
    • rotation
  • The pure rotation sequence has only normal speed, fast speed types

Downloads

You can download a single ROS bag file from the link below. (or whole dataset from KAIST VIO dataset)

Trajectory Type ROS bag download
circle normal
fast
rotation		 link
link
link
infinity normal
fast
rotation		 link
link
link
square normal
fast
rotation		 link
link
link
rotation normal
fast		 link
link



Dataset format


  • Each set of data is recorded as a ROS bag file.
  • Each data sequence contains the followings:
    • stereo infra images (w/ emitter turned off)
    • mono RGB image
    • IMU data (3-axes accelerometer, 3-axes gyroscopes)
    • 6-DOF Ground-Truth
  • ROS topic
    • Camera(30 Hz): "/camera/infra1(2)/image_rect_raw/compressed", "/camera/color/image_raw/compressed"
    • IMU(100 Hz): "/mavros/imu/data"
    • Ground-Truth(50 Hz): "/pose_transformed"
  • In the config directory
    • trans-mat.yaml: translational matrix between the origin of the Ground-Truth and the VI sensor unit.
      (the offset has already been applied to the bag data, and this YAML file has estimated offset values, just for reference. To benchmark your VO/VIO method more accurately, you can use your alignment method with other tools, like origin alignment or Umeyama alignment from evo)
    • imu-params.yaml: estimated noise parameters of Pixhawk 4 mini
    • cam-imu.yaml: Camera intrinsics, Camera-IMU extrinsics in kalibr format
  • publish ground truth as trajectory ROS topic
    • ground truth are recorded as 'geometry_msgs/PoseStamped'
    • but you may want to acquire 'nav_msgs/Path' rather than just 'Pose' for visuaslization purpose (e.g. Rviz)
    • For this, you can refer this package for 'geometry_msgs/PoseStamped'->'nav_msgs/Path': tf_to_trajectory



Setup

- Hardware


                                                            Fig.1 Lab Environment                                        Fig.2 UAV platform
  • VI sensor unit
    • camera: Intel Realsense D435i (640x480 for infra 1,2 & RGB images)
    • IMU: Pixhawk 4 mini
    • VI sensor unit was calibrated by using kalibr

  • Ground-Truth
    • OptiTrack PrimeX 13 motion capture system with six cameras was used
    • including 6-DOF motion information.

- Software (VO/VIO Algorithms): How to set each (publicly available) algorithm on the jetson board

VO/VIO Setup link
VINS-Mono link
ROVIO link
VINS-Fusion link
Stereo-MSCKF link
Kimera link

Citation

If you use the algorithm in an academic context, please cite the following publication:

@article{jeon2021run,
  title={Run your visual-inertial odometry on NVIDIA Jetson: Benchmark tests on a micro aerial vehicle},
  author={Jeon, Jinwoo and Jung, Sungwook and Lee, Eungchang and Choi, Duckyu and Myung, Hyun},
  journal={IEEE Robotics and Automation Letters},
  volume={6},
  number={3},
  pages={5332--5339},
  year={2021},
  publisher={IEEE}
}

Lisence

This datasets are released under the Creative Commons license (CC BY-NC-SA 3.0), which is free for non-commercial use (including research).