A central challenge for multi-robot systems is fusing independently gathered perception data into a unified representation. Despite progress in Collaborative SLAM (C-SLAM), benchmarking remains hindered by the scarcity of dedicated multi-robot datasets. Many evaluations instead partition single-robot trajectories, a practice that may only partially reflect true multi-robot operations and, more critically, lacks standardization, leading to results that are difficult to interpret or compare across studies. While several multi-robot datasets have recently been introduced, they mostly contain short trajectories with limited inter-robot overlap and sparse intra-robot loop closures. To overcome these limitations, we introduce CU-Multi, a dataset collected over multiple days at two large outdoor sites on the University of Colorado Boulder campus. CU-Multi comprises four synchronized runs with aligned start times and controlled trajectory overlap, replicating the distinct perspectives of a robot team. It includes RGB-D sensing, RTK GPS, semantic LiDAR, and refined ground-truth odometry. By combining overlap variation with dense semantic annotations, CU-Multi provides a strong foundation for reproducible evaluation in multi-robot collaborative perception tasks.

翻译：多机器人系统的核心挑战在于将独立采集的感知数据融合为统一的表征。尽管协作式SLAM（C-SLAM）领域已取得进展，但专用多机器人数据集的匮乏始终制约着基准测试的发展。当前多数评估仍采用分割单机器人轨迹的方法，这种实践可能仅部分反映真实的多机器人作业场景，更关键的是缺乏标准化，导致研究结果难以解读或跨研究对比。虽然近年涌现出若干多机器人数据集，但其内容多为短程轨迹，存在机器人间重叠区域有限、单机器人内回环检测稀疏等问题。为突破这些局限，我们提出CU-Multi数据集——该数据集在科罗拉多大学博尔德分校两个大型户外场地历经多日采集而成。CU-Multi包含四组同步运行的序列，具有对齐的起始时间与可控的轨迹重叠度，真实复现了机器人团队的差异化视角。数据集涵盖RGB-D感知、RTK GPS、语义LiDAR及高精度真值里程计。通过融合轨迹重叠度变化与密集语义标注，CU-Multi为多机器人协同感知任务的可重复性评估奠定了坚实基础。