CAVERS: Multimodal SLAM Data from a Natural Karstic Cave with Ground Truth Motion Capture

TL;DR

CAVERS is a comprehensive multimodal dataset from a natural karstic cave, enabling evaluation of SLAM and odometry algorithms in challenging subterranean environments with ground truth data.

Contribution

The paper introduces CAVERS, a new multimodal dataset with ground truth for SLAM research in complex cave environments, filling a significant gap in available data.

Findings

Benchmarking seven SLAM and odometry algorithms demonstrates dataset's utility.

Dataset includes diverse sensing modalities under full darkness and artificial illumination.

Provides mm-accurate ground truth poses for robust evaluation.

Abstract

Autonomous robots operating in natural karstic caves face perception and navigation challenges that are qualitatively distinct from those encountered in mines or tunnels: irregular geometry, reflective wet surfaces, near-zero ambient light, and complex branching passages. Yet publicly available datasets targeting this environment remain scarce and offer limited sensing modalities and environmental diversity. We present CAVERS, a multimodal dataset acquired in two structurally distinct rooms of Cueva de la Victoria, M\'alaga, Spain, comprising 24 sequences totaling approximately 335 GB of recorded data. The sensor suite combines an Intel RealSense D435i RGB-D-I camera, an Optris PI640i near-IR thermal camera, and a Velodyne VLP-16 LiDAR, operated both handheld and mounted on a wheeled rover under full darkness and artificial illumination. For most of the sequences, mm-accurate 6-DoF ground truth pose and velocity at 120 Hz are provided by an Optirack motion capture system installed directly inside the cave. We benchmark seven state-of-the-art SLAM and odometry algorithms spanning visual, visual-inertial, thermal-inertial, and LiDAR-based pipelines, as well as a 3D reconstruction pipeline, demonstrating the dataset's usability. %The dataset and all supplementary material are publicly available at: https://github.com/spaceuma/cavers.