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…
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