This is a repository for the VIODE (Visual-Inertial Odometry in Dynamic Environments) dataset described in the paper:

Koji Minoda, Fabian Schilling, Valentin Wüest, Dario Floreano, and Takehisa Yairi, VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments, IEEE Robotics and Automation Letters (RA-L), 2021. PDF

The overall documentation is available in the above RA-L paper. If you use VIODE in academic work, please cite:

@article{minodaRAL2021,
  title={{VIODE}: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments},
  author={Minoda, Koji, and Schilling, Fabian, and W\"{u}est, Valentin, and Floreano, Dario, and Yairi, Takehisa},
  journal={IEEE Robotics and Automation Letters},
  year={2021},
  volume={6},
  number={2},
  pages={1343-1350},
  doi={10.1109/LRA.2021.3058073}}
 }

A YouTube video for an introduction to the VIODE dataset:

Main ROS bag files are uploaded on Zenodo:
Data download here (A link to Zenodo)

The other two files can be downloaded from this repository.

The visual-inertial sensor data is provided in ROS bag format. Each bag contains the following topics.

• /cam0/image_raw
• /cam1/image_raw
• /cam0/segmentation
• /cam1/segmentation
• /imu0
• /odometry

/cam0/image_raw and /cam1/image_raw contain RGB image data. Since these are captured in the simulator, we also provide ground-truth extrinsic and intrinsic parameters for this stereo setup.

/cam0/segmentation and /cam1/segmentation are the ground truth semantic segmentation provided by AirSim. The ex-/intrinsic parameters are the same as the ones with the RGB images.

The other files are

• calibration.yaml
• cam0_pinhole.yaml & cam1_pinhole.yaml
• rgb_id.txt
• vehicle_ids_*.txt

The calibration.yaml, cam0_pinhole.yaml, and cam1_pinhole.yaml provide extrinsic and intrinsic parameters of two cameras. Note that the format follows that of VINS-Fusion, one of the state-of-the-art VIO algorithms. The rgb_id.txt provides the correspondence between object ID and RGB value in segmentation images. The vehicle_ids_*.txt contains the correspondence between object ID and object name for each environment. Object name also indicates whether the vehicle is whether dynamic or static.