S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866917826030206976 |
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| author | Feng, Dapeng Qi, Yuhua Zhong, Shipeng Chen, Zhiqiang Chen, Qiming Chen, Hongbo Wu, Jin Ma, Jun |
| author_facet | Feng, Dapeng Qi, Yuhua Zhong, Shipeng Chen, Zhiqiang Chen, Qiming Chen, Hongbo Wu, Jin Ma, Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_13723 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM Feng, Dapeng Qi, Yuhua Zhong, Shipeng Chen, Zhiqiang Chen, Qiming Chen, Hongbo Wu, Jin Ma, Jun Robotics Computer Vision and Pattern Recognition 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. |
| title | S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2210.13723 |