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

TL;DR

The S3LI Vulcano dataset offers multi-modal visual and LiDAR data from volcanic environments to facilitate SLAM and place recognition research in unstructured terrains.

Contribution

It introduces a new multi-modal dataset with diverse volcanic environments and an open-source toolkit for ground truth generation and data preparation.

Findings

Provides diverse volcanic environment data for SLAM benchmarking

Includes tools for ground truth and label generation

Enables testing of multi-modal SLAM algorithms

Abstract

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.