CAVERS: Multimodal SLAM Data from a Natural Karstic Cave with Ground Truth Motion Capture
This dataset fills a gap for autonomous robot navigation in natural caves, but it is an incremental contribution as it primarily offers new data rather than novel methods.
CAVERS provides a multimodal SLAM dataset from a natural karstic cave with ground truth motion capture, addressing the lack of diverse sensing data for this challenging environment. Benchmarking seven SLAM algorithms shows the dataset's usability, though no specific performance numbers are reported.
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, Málaga, 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.