构建首个大规模多模态越野导航3D可通行数据集，自动标注地形可通行性。
STONE Dataset: A Scalable Multi-Modal Surround-View 3D Traversability Dataset for Off-Road Robot Navigation
- 全自动无标注管道生成3D可通行地图，基于激光雷达重建地形并计算坡度、粗糙度等属性。
- 包含128通道激光雷达、6个摄像头和3个4D雷达的同步多模态数据，覆盖昼夜与多种地形。
- 适合研究自动驾驶、机器人导航的学者，尤其关注多传感器融合与可通行性预测者。
可靠的非结构化环境导航依赖于对可通行区域的精准估计以及在复杂地形和感知条件下的鲁棒感知。然而，现有数据集在可扩展性和多模态覆盖方面存在不足，制约了3D可通行性预测的发展。本文提出STONE，一个大规模多模态越野导航数据集。该数据集提供：（1）由全自动、无需人工标注的流程生成的轨迹引导型3D可通行地图；（2）涵盖128通道激光雷达、六个RGB相机和三个4D成像雷达的同步环视传感数据。数据覆盖昼夜、草地、农田、工地及湖泊等多种场景。我们的自动标注管道通过激光雷达扫描重建密集地形表面，提取坡度、高程、粗糙度等几何特征，并基于马氏距离准则将可通行标签延伸至机器人轨迹之外。该设计实现了无需人工标注的可扩展、基于几何信息的真值构建。最后，我们建立了体素级3D可通行性预测基准，并提供了单模态与多模态设置下的强基线模型。STONE数据集已公开：https://konyul.github.io/STONE-dataset/
原文摘要 · Abstract (English)
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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