  The burgeoning demand for collaborative robotic systems to execute complex
tasks collectively has intensified the research community's focus on advancing
simultaneous localization and mapping (SLAM) in a cooperative context. Despite
this interest, the scalability and diversity of existing datasets for
collaborative trajectories remain limited, especially in scenarios with
constrained perspectives where the generalization capabilities of Collaborative
SLAM (C-SLAM) are critical for the feasibility of multi-agent missions.
Addressing this gap, we introduce S3E, an expansive multimodal dataset.
Captured by a fleet of unmanned ground vehicles traversing four distinct
collaborative trajectory paradigms, S3E encompasses 13 outdoor and 5 indoor
sequences. These sequences feature meticulously synchronized and spatially
calibrated data streams, including 360-degree LiDAR point cloud,
high-resolution stereo imagery, high-frequency inertial measurement units
(IMU), and Ultra-wideband (UWB) relative observations. Our dataset not only
surpasses previous efforts in scale, scene diversity, and data intricacy but
also provides a thorough analysis and benchmarks for both collaborative and
individual SLAM methodologies. For access to the dataset and the latest
information, please visit our repository at https://pengyu-team.github.io/S3E.
