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

2026-04-16

AI Summary

This work addresses the significant challenges in robotic perception and navigation within natural karst caves, characterized by irregular geometries, slippery reflective surfaces, near-zero illumination, and complex branching structures. To bridge the lack of benchmark data for such extreme environments, we present the first publicly available multimodal SLAM dataset, collected in real cave settings in Spain. The dataset integrates synchronized RGB-D (Intel RealSense D435i), thermal imaging (Optris PI640i), LiDAR (Velodyne VLP-16), and high-precision OptiTrack motion capture data providing millimeter-level ground-truth poses. It comprises 24 sequences—captured using both handheld and wheeled platforms under complete darkness and artificial lighting—totaling approximately 335 GB. This resource supports diverse SLAM paradigms and has been used to successfully evaluate seven state-of-the-art algorithms, thereby filling a critical gap in multimodal localization and mapping research under extreme conditions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance

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, 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.

Problem

• karstic cave
• autonomous navigation
• perception challenge
• multimodal dataset
• SLAM

Innovation

• multimodal SLAM
• karstic cave dataset
• ground truth motion capture
• thermal-inertial odometry
• low-light navigation