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The EuRoC micro aerial vehicle datasets
Abstract
This paper presents visual-inertial datasets collected on-board a micro aerial vehicle. The datasets contain synchronized stereo images, IMU measurements and accurate ground truth. The first batch of datasets facilitates the design and evaluation of visual-inertial localization algorithms on real flight data. It was collected in an industrial environment and contains millimeter accurate position ground truth from a laser tracking system. The second batch of datasets is aimed at precise 3D environment reconstruction and was recorded in a room equipped with a motion capture system. The datasets contain 6D pose ground truth and a detailed 3D scan of the environment. Eleven datasets are provided in total, ranging from slow flights under good visual conditions to dynamic flights with motion blur and poor illumination, enabling researchers to thoroughly test and evaluate their algorithms. All datasets contain raw sensor measurements, spatio-temporally aligned sensor data and ground truth, extrinsic and intrinsic calibrations and datasets for custom calibrations.
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Cited by
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Mur-Artal, R., & Tardos, J. D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2705103 · Cited by 6,898
“The recent EuRoC dataset [21] contains 11 stereo sequences recorded from a microaerial vehicle (MAV) flying around two different rooms and a large industrial environment.”
BackgroundVINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
Qin, T., Li, P., & Shen, S. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2853729 · Cited by 4,669
(2020) · doi:10.1109/icra40945.2020.9196885 · Cited by 633
Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback
Bloesch, M., Burri, M., Omari, S., Hutter, M., & Siegwart, R. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917728574 · Cited by 485
Self-citationTartanAir: A Dataset to Push the Limits of Visual SLAM
Wang, W., Zhu, D., Wang, X., Hu, Y., Qiu, Y., Wang, C., Hu, Y., Kapoor, A., & Scherer, S. (2020) · 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341801 · Cited by 456
Metadata
- Authors
- Burri, M., Nikolic, J., Gohl, P., Schneider, T., Rehder, J., Omari, S., Achtelik, M. W., & Siegwart, R.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2016-01-25
- Volume
- 35
- Issue
- 10
- Pages
- 1157-1163
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2396274919
- Cited by
- 2,066
- References
- 0
- FWCI
- 131.22
- Type
- journal article
- Language
- en
- Open access
- closed
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- Burri, M., Nikolic, J., Gohl, P., Schneider, T., Rehder, J., Omari, S., Achtelik, M. W., & Siegwart, R. (2016). The EuRoC micro aerial vehicle datasets. The International Journal of Robotics Research. https://doi.org/10.1177/0278364915620033
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