These datasets include scans of subterranean environments, robot mapping collections, etc.
Scan data of the Systems Competition courses at the NIOSH mine in Pittsburgh, PA collected by DARPA.
Tags: tunnel, environment
Scan data of the Systems Competition courses at the Satsop Nuclear Power Plant in Elma, WA collected by DARPA.
Tags: urban, environment
Scan data of the Systems Competition course at the Louisville Megacavern in Louisville, KY collected by DARPA.
Tags: finals, environment
Point cloud data sampled from the Virtual Competition world meshes.
Tags: tunnel, urban, cave, finals, simulation 🖥️
Robot data collected by the Army Research Laboratory in the Systems Competition courses at STIX, Tunnel Circuit, and Urban Circuit events. The data includes rosbags with LIDAR, camera, odometry, and mapping data as robots were teleoperated through the courses to known artifact locations.
Tags: stix, tunnel, urban, robot 🤖
Publications:
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Rogers, John & Gregory, Jason & Fink, Jonathan & Stump, Ethan. (2020). Test Your SLAM! The SubT-Tunnel dataset and metric for mapping. 955-961. 10.1109/ICRA40945.2020.9197156.
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Rogers J.G. et al. (2021) The DARPA SubT Urban Circuit Mapping Dataset and Evaluation Metric. In: Siciliano B., Laschi C., Khatib O. (eds) Experimental Robotics. ISER 2020. Springer Proceedings in Advanced Robotics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-71151-1_35
The Colorado mm-Wave Radar (ColoRadar) dataset by Team MARBLE consists of 52 sequences, recorded in mines, built environments, and in an urban creek path, totaling more than 145 minutes of 3D FMCW radar, 3D lidar, and IMU data.
Tags: tunnel, urban
The SubT-MRS Dataset is a collection of multi-robot datasets by Team Explorer obtained from caves, urban areas, and tunnels. The datasets are multi-modal, integrating visual, lidar, thermal, and inertial measurements, enabling exploration under demanding conditions such as darkness, smoke, dust, and geometrically degraded environments.
Tags: tunnel, urban, cave, robot 🤖
Publications:
- Zhao, Shibo, et al. (2023) SubT-MRS: A subterranean, multi-robot, multi-spectral and multi-degraded dataset for robust SLAM. arXiv preprint arXiv:2307.07607. https://doi.org/10.48550/arXiv.2307.07607