We release a dataset recorded on the Moon-like environment of Mount Etna, Sicily, with a sensor setup that comprises a stereo camera, a LiDAR and an IMU. The dataset is intended to expose several factors that challenge visual- and LiDAR-based localization and mapping pipelines, when operating in severely unstructured environments. The harsh lighting conditions, combined with extreme visual aliasing, as well as the lack of salient structures, limit the possibility of performing place recognition through traditional approaches. Furthermore, the LiDAR sensor, employed in this dataset, is built relying on novel Solid-State technologies, that have promising characteristics for their implementation in space applications. The narrow Field-of-View (~70H x 30V) of the LiDAR, combined with the geometry of the landscape, does not allow to implement traditional LiDAR SLAM, and require to investigate the usage of the sensor as a complement to the visual inputs. This dataset offers 7 sequences, with peculiar characteristics in terms of type of trajectories and type of landscape, with accurate D-GNSS ground truth.

Sequences, ground truth and configuration files, are accessible at:



Riccardo Giubilato, Wolfgang Stürzl, Armin Wedler, Rudolph Triebel (2022) Challenges of SLAM in Extremely Unstructured Environments: The DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset. IEEE Robotics and Automation Letters, 7 (4), pp. 8721-8728. IEEE – Institute of Electrical and Electronics Engineers. doi: 10.1109/LRA.2022.3188118. ISSN 2377-3766. [elib]

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