构建大规模多模态导航数据集，支持机器人在校园环境自主行走
GND: Global Navigation Dataset with Multi-Modal Perception and Multi-Category Traversability in Outdoor Campus Environments
- 融合激光雷达、可见光与全景图像的多模态感知数据
- 覆盖2.7km²校园区域，包含5类通行性地图和350栋建筑
- 适用于无图导航、全局定位等机器人自主导航任务
在大型户外环境中导航需综合考虑几何结构、环境语义与地形特征，通常依赖车载传感器如激光雷达和摄像头获取。尽管现有移动机器人可基于手工规则的高精度地图在特定环境中导航，但缺乏人类在未知户外空间中所具备的常识推理能力。为此，我们提出了全球导航数据集（GND），一个大规模数据集，整合了多模态传感数据，包括3D激光雷达点云、RGB图像与360度全景图像，以及来自十所大学校园的多类别通行性地图（人行道、车行道、台阶、非铺装地面和障碍物）。这些环境涵盖多种公园、城市场景、高程变化及不同规模的校园布局，总面积约2.7km²，共包含至少350栋建筑。我们还展示了GND在地图引导导航、无地图导航和全局场景识别等新应用中的实用性。
原文摘要 · Abstract (English)
Navigating large-scale outdoor environments requires complex reasoning in terms of geometric structures, environmental semantics, and terrain characteristics, which are typically captured by onboard sensors such as LiDAR and cameras. While current mobile robots can navigate such environments using pre-defined, high-precision maps based on hand-crafted rules catered for the specific environment, they lack commonsense reasoning capabilities that most humans possess when navigating unknown outdoor spaces. To address this gap, we introduce the Global Navigation Dataset (GND), a large-scale dataset that integrates multi-modal sensory data, including 3D LiDAR point clouds and RGB and 360-degree images, as well as multi-category traversability maps (pedestrian walkways, vehicle roadways, stairs, off-road terrain, and obstacles) from ten university campuses. These environments encompass a variety of parks, urban settings, elevation changes, and campus layouts of different scales. The dataset covers approximately 2.7km2 and includes at least 350 buildings in total. We also present a set of novel applications of GND to showcase its utility to enable global robot navigation, such as map-based global navigation, mapless navigation, and global place recognition.
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