STONE Dataset: A Scalable Multi-Modal Surround-View 3D Traversability Dataset for Off-Road Robot Navigation

• URL: http://arxiv.org/abs/2603.09175v2
• Date: Thu, 12 Mar 2026 07:59:57 GMT
• Title: STONE Dataset: A Scalable Multi-Modal Surround-View 3D Traversability Dataset for Off-Road Robot Navigation
• Abstract summary: STONE is a large-scale multi-modal dataset for off-road navigation.<n>The dataset covers a wide range of environments and conditions, including day and night, grasslands, farmlands, construction sites, and lakes.<n>We establish a benchmark for voxel-level 3D traversability prediction and provide strong baselines under both single-modal and multi-modal settings.
• Score: 20.183016318362558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable off-road navigation requires accurate estimation of traversable regions and robust perception under diverse terrain and sensing conditions. However, existing datasets lack both scalability and multi-modality, which limits progress in 3D traversability prediction. In this work, we introduce STONE, a large-scale multi-modal dataset for off-road navigation. STONE provides (1) trajectory-guided 3D traversability maps generated by a fully automated, annotation-free pipeline, and (2) comprehensive surround-view sensing with synchronized 128-channel LiDAR, six RGB cameras, and three 4D imaging radars. The dataset covers a wide range of environments and conditions, including day and night, grasslands, farmlands, construction sites, and lakes. Our auto-labeling pipeline reconstructs dense terrain surfaces from LiDAR scans, extracts geometric attributes such as slope, elevation, and roughness, and assigns traversability labels beyond the robot's trajectory using a Mahalanobis-distance-based criterion. This design enables scalable, geometry-aware ground-truth construction without manual annotation. Finally, we establish a benchmark for voxel-level 3D traversability prediction and provide strong baselines under both single-modal and multi-modal settings. STONE is available at: https://konyul.github.io/STONE-dataset/

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