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FEE Corridor

Introduced in Self-Supervised Depth Correction of Lidar Measurements from Map Consistency Loss2 Mar 2023 archive 2025-07-28

The data set contains point cloud data captured in an indoor environment with precise localization and ground truth mapping information. Two ”stop-and-go” data sequences of a robot with mounted Ouster OS1-128 lidar are provided. This data-capturing strategy allows recording lidar scans that do not suffer from an error caused by sensor movement. Individual scans from static robot positions are recorded. Additionally, point clouds recorded with the Leica BLK360 scanner are provided as mapping ground-truth data.

Benchmarks archive 2025-07-28

No leaderboard in the archive resolves to this dataset.

Papers archive 2025-07-28

No paper in the archive has a leaderboard row on this dataset; the archive counts 1 paper for it but never published that list.

Dataset loaders archive 2025-07-28

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

BSD 3-Clause

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • FEE Corridor

1 variant name, as the archive lists them.

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