Research/ SPACE AI & AUTONOMY

The S3LI Vulcano Dataset: A Dataset for Multi-Modal SLAM in Unstructured Planetary Environments

Riccardo Giubilato, Marcus Gerhard Müller, Marco Sewtz, Laura Alejandra Encinar Gonzalez, John Folkesson, Rudolph Triebel

IEEE Aerospace 2026/Jan 27, 2026/ 5 citations

Space × Robotics × AI

Technique

Perception & State EstimationarXiv: cs.CV · Simulation, Digital Twins & Benchmarkstext match · Learning-based MethodsOpenAlex topic: Reinforcement Learning in Robotics

Indexed as

arXivcs.CV · cs.ROOpenAlexRobotics and Sensor-Based Localization · 3D Surveying and Cultural Heritage · Reinforcement Learning in Robotics

HTML PDF arXiv:2601.19557 DOI Code Semantic Scholar OpenAlex

We release the S3LI Vulcano dataset, a multi-modal dataset towards development and benchmarking of Simultaneous Localization and Mapping (SLAM) and place recognition algorithms that rely on visual and LiDAR modalities. Several sequences are recorded on the volcanic island of Vulcano, from the Aeolian Islands in Sicily, Italy. The sequences provide users with data from a variety of environments, textures and terrains, including basaltic or iron-rich rocks, geological formations from old lava channels, as well as dry vegetation and water. The data (rmc.dlr.de/s3li_dataset) is accompanied by an open source toolkit (github.com/DLR-RM/s3li-toolkit) providing tools for generating ground truth poses as well as preparation of labelled samples for place recognition tasks.

Comments: Accepted submission to the 2026 IEEE Aerospace Conference

The S3LI Vulcano Dataset: A Dataset for Multi-Modal SLAM in Unstructured Planetary Environments

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