构建首个空地协同地形可通行性数据集，支持真实野外环境下的多模态协作感知研究。
GA3T: A Ground-Aerial Terrain Traversability Dataset for Heterogeneous Robot Teams in Unstructured Environments
- 空地机器人同步采集多模态数据，覆盖森林、岩石、泥地等复杂地形
- 包含超1.3万帧同步图像与8000+人工标注，支持零样本分割与可通行性分析
- 春季稀疏树冠设计使空中视角可穿透遮挡，适合研究遮挡感知与跨视图融合
异构空地机器人团队结合互补的感知模态、移动特性和空间视角，显著提升复杂户外环境下的感知能力。然而，多机器人协同感知的发展受限于缺乏真实世界中多平台在非结构化地形上重叠多模态观测的数据集。本文提出GA3T（Ground-Aerial Team for Terrain Traversal）数据集，使用Clearpath Husky地面机器人和Autel EVO II无人机，在森林小径、岩石路径、泥泞地形、雪堆及草地等4种不同环境中采集数据。地面平台提供3D LiDAR、双目相机、IMU和GPS数据，空中平台则贡献RGB图像、热成像/红外数据及来自上方视角的GPS信息，实现丰富的跨模态、跨视角感知。数据集涵盖约29分钟运行时间，超过13,000帧同步帧，并包含基于SAM 3的零样本分割结果和超过8,000张人工标注图像。其独特之处在于采集时间为早春，树冠稀疏，使得空中平台能部分透过树叶观测地面机器人与地形，支持遮挡感知下的协同感知研究。不同于以往聚焦于SLAM或模拟协同驾驶的多机器人数据集，GA3T专为真实野外环境中的跨视图感知、空地视角融合、可通行性估计和协同场景理解研究而设计。
原文摘要 · Abstract (English)
Heterogeneous air-ground robot teams combine complementary sensing modalities, mobility characteristics, and spatial viewpoints that can significantly enhance perception in complex outdoor environments. However, progress in multi-robot collaborative perception has been constrained by the lack of real-world datasets featuring overlapping multi-modal observations from platforms operating in unstructured terrain. We present GA3T (Ground-Aerial Team for Terrain Traversal), a real-world multi-robot collaborative perception dataset collected using a Clearpath Husky UGV and an Autel EVO~II UAV across diverse unstructured environments, including forest trails, rocky paths, muddy terrain, snow piles, and grass-covered fields. The ground platform provides 3D LiDAR, stereo camera, IMU, and GPS data, while the aerial platform contributes RGB imagery, thermal/infrared observations, and GPS from a complementary overhead viewpoint, allowing for rich cross-modal and cross-view perception. The dataset is collected in 4 unique environments, with over 13,000 synchronized frames across approximately 29 minutes of operation, and includes both SAM~3-based zero-shot segmentation and over 8,000 manually labeled images. A unique aspect of the dataset is its early-spring collection period, during which sparse tree canopies allow the aerial robot to partially observe the ground robot and terrain through the trees, allowing for occlusion-aware collaborative perception. Unlike prior multi-robot datasets that focus on SLAM or simulated cooperative driving, GA3T is specifically designed to support research on cross-view perception, air-ground viewpoint fusion, traversability estimation, and collaborative scene understanding in real off-road environments.
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