[Software] Dataset Release on: Large UAV multisensor dataset with outdoor ground truth, indoor-outdoor transitions, and indoor data

The Control of Networked Systems group (University of Klagenfurt, Austria) has just published a pre-print and the data for the INSANE data set (INSANE: Cross-Domain UAV Data Sets with Increased Number of Sensors for developing Advanced and Novel Estimators).

The preprint is available here [2210.09114] INSANE: Cross-Domain UAV Data Sets with Increased Number of Sensors for developing Advanced and Novel Estimators
and the entire data is available on our webpage https://www.aau.at/intelligente-systemtechnologien/control-of-networked-systems/datasets/

This data set contains over 600GB of multimodal data from a Mars analog mission, including accurate 6DoF outdoor ground truth, indoor-outdoor transitions with continuous cross-domain ground truth, and indoor data with Optitrack measurements as ground truth. With 26 flights, this data set provides you with various distinct challenges for testing and proofing your algorithms.

The UAV carries 18 sensors, including a high-resolution navigation camera and a stereo camera with an overlapping field of view, two RTK GNSS sensors with centimeter accuracy, as well as three IMUs, placed at strategic locations: Hardware dampened at the center, off-center with a lever arm, and a 1kHz IMU rigidly attached to the UAV (in case you want to work with unfiltered data).

The sensors are fully pre-calibrated, and the data set is ready to use. However, if you want to use your own calibration algorithms, then the raw calibration data is also ready for download.

The cross-domain outdoor to indoor transition segments are especially challenging because of realistic sensor behavior such as GNSS degradation and dropouts, changes in the measured magnetic field, and changing light conditions when transitioning to the indoor environment.

The data set also provides 4 hours of static sensor data and vibration data with accurate RPM measurements to provide you with additional information about sensor properties and vehicle integrity.

The data set provides you with everything you need to test your research: First, you can test your algorithms with the indoor data sets in a controlled environment and then switch to the more challenging flight scenario, such as the transition data, which requires sensor switching, or the Mars analog data with higher velocities, multiple touchdowns, challenging ground structures or constant velocity segments. The Mars analog data also contains cliff flight overs and traverses while the stereo camera faces the cliff in case you want to perform a 3D reconstruction or challenge your SLAM algorithm.

The critical aspects of each data set are shown on the website, making it easy to find the best data to test or challenge your algorithm.

Looking forward to your feedback and results using this data for your algorithms and robots,
Christian Brommer, Alessandro Fornasier, Martin Scheiber, Jeff Delaune, Roland Brockers, Jan Steinbrener, Stephan Weiss


Univ.-Prof. DI Dr. Stephan Weiss
Head of Institute of Smart System Technologies
Control of Networked Systems Group
University of Klagenfurt
Universit├Ątsstra├če 65-67
9020 Klagenfurt
Austria

T. +43 463/2700-3571
F. +43 463/2700 993571
M. stephan.weiss@aau.at
W. sst.aau.at/cns
W. uav.aau.at
Y. www.youtube.com/channel/UCAlDLtj_iHDOSCFz2fzQxWw

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