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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

TL;DR

The paper presents the S3LI Vulcano dataset, a multi-modal SLAM benchmark designed for planetary-like, unstructured environments. It combines RGB stereo, a solid-state LiDAR, IMU, and differential GNSS, all time-synchronized and packaged as ROS bagfiles, with seven diverse sequences collected on Vulcano Island. It details sensor calibration (camera and IMU), offline ground-truth generation via RTKLIB, and a toolkit for ground-truth-aware place-recognition experiments, plus baseline SLAM evaluations to illustrate applicability. The work aims to enable robust localization and mapping in natural, geologically varied terrains, supporting both SLAM development and cross-modality place recognition for planetary exploration scenarios.

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.

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

TL;DR

The paper presents the S3LI Vulcano dataset, a multi-modal SLAM benchmark designed for planetary-like, unstructured environments. It combines RGB stereo, a solid-state LiDAR, IMU, and differential GNSS, all time-synchronized and packaged as ROS bagfiles, with seven diverse sequences collected on Vulcano Island. It details sensor calibration (camera and IMU), offline ground-truth generation via RTKLIB, and a toolkit for ground-truth-aware place-recognition experiments, plus baseline SLAM evaluations to illustrate applicability. The work aims to enable robust localization and mapping in natural, geologically varied terrains, supporting both SLAM development and cross-modality place recognition for planetary exploration scenarios.

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.
Paper Structure (13 sections, 3 figures, 4 tables)

This paper contains 13 sections, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Unstructured and Natural Terrestrial Environments
  4. Planetary-Like Environments
  5. The S3Li Vulcano Dataset
  6. Sensor Setup
  7. Calibration and Post-Processing
  8. Camera Calibration
  9. IMU-Camera Calibration
  10. Generation of Differential GNSS Solution for Ground Truth
  11. Example Applications
  12. Multi-modal Place Recognition
  13. Conclusions

Figures (3)

  • Figure 1: Impression of the S3LI (RGB Stereo, Solid-State LiDAR, Inertial) sensor setup, captured on the Gran Cratere della Fossa, the active center of Vulcano, vulcan from the homonymous island from the Aeolian Islands, Sicily. On the setup it is visible the white GNSS antenna, computer unit from an Intel NUC and touchscreen to operate the sensor setup.
  • Figure 2: Graphical examples of associated RGB images and LiDAR scans from the dataset sequences. For each sequence, 3 samples of corresponding images and scans are provided
  • Figure 3: Example View from the place recognition toolkit. On the left, a clickable overlap matrix shows, with colors from blue to yellow, the amount of overlap between two views. When clicked, the corresponding position and FoV (Field of View) of the two images is shown on the map in the center, while images, pointcloud overlaps and point depth histograms are shown on the right.