In the past decade, although single-robot perception has made significant
advancements, the exploration of multi-robot collaborative perception remains
largely unexplored. This involves fusing compressed, intermittent, limited,
heterogeneous, and asynchronous environmental information across multiple
robots to enhance overall perception, despite challenges like sensor noise,
occlusions, and sensor failures. One major hurdle has been the lack of
real-world datasets. This paper presents a pioneering and comprehensive
real-world multi-robot collaborative perception dataset to boost research in
this area. Our dataset leverages the untapped potential of air-ground robot
collaboration featuring distinct spatial viewpoints, complementary robot
mobilities, coverage ranges, and sensor modalities. It features raw sensor
inputs, pose estimation, and optional high-level perception annotation, thus
accommodating diverse research interests. Compared to existing datasets
predominantly designed for Simultaneous Localization and Mapping (SLAM), our
setup ensures a diverse range and adequate overlap of sensor views to
facilitate the study of multi-robot collaborative perception algorithms. We
demonstrate the value of this dataset qualitatively through multiple
collaborative perception tasks. We believe this work will unlock the potential
research of high-level scene understanding through multi-modal collaborative
perception in multi-robot settings.

过去十年中，虽然单一机器人感知取得了重大进展，但多机器人协同感知的研究仍未被广泛探索。本文提出了一个开创性的全面真实世界的多机器人协同感知数据集，用于推动这一领域的研究。我们的数据集利用了空中 - 地面机器人协同的未发掘潜力，具有不同的空间视角、互补的机器人移动性、覆盖范围和传感器模态。它提供了原始传感器输入、姿态估计和可选的高级感知注释，以适应各种研究兴趣。与现有主要设计用于同时定位和地图构建（SLAM）的数据集相比，我们的设置确保了多样化的传感器视角范围和足够的重叠以促进多机器人协同感知算法的研究。我们通过多个协同感知任务定性地证明了这个数据集的价值。我们相信这项工作将在多模态协同感知中解锁高级场景理解的潜在研究。