With the advanced request to employ a team of robots to perform a task
collaboratively, the research community has become increasingly interested in
collaborative simultaneous localization and mapping. Unfortunately, existing
datasets are limited in the scale and variation of the collaborative
trajectories, even though generalization between inter-trajectories among
different agents is crucial to the overall viability of collaborative tasks. To
help align the research community's contributions with realistic multiagent
ordinated SLAM problems, we propose S3E, a large-scale multimodal dataset
captured by a fleet of unmanned ground vehicles along four designed
collaborative trajectory paradigms. S3E consists of 7 outdoor and 5 indoor
sequences that each exceed 200 seconds, consisting of well temporal
synchronized and spatial calibrated high-frequency IMU, high-quality stereo
camera, and 360 degree LiDAR data. Crucially, our effort exceeds previous
attempts regarding dataset size, scene variability, and complexity. It has 4x
as much average recording time as the pioneering EuRoC dataset. We also provide
careful dataset analysis as well as baselines for collaborative SLAM and single
counterparts. Data and more up-to-date details are found at
https://github.com/PengYu-Team/S3E