S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM
This dataset addresses a bottleneck for researchers in multi-robot systems by enabling more robust testing and development of collaborative SLAM algorithms, though it is incremental as it builds on existing data collection efforts.
The authors tackled the lack of scalable and diverse datasets for collaborative SLAM by introducing S3E, a multimodal dataset with 18 sequences from outdoor and indoor environments, which includes synchronized data streams like LiDAR and stereo imagery, and provides benchmarks for SLAM methods.
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.