Swarm-LIO2: Decentralized, Efficient LiDAR-inertial Odometry for UAV Swarms
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916411888107520 |
|---|---|
| author | Zhu, Fangcheng Ren, Yunfan Yin, Longji Kong, Fanze Liu, Qingbo Xue, Ruize Liu, Wenyi Cai, Yixi Lu, Guozheng Li, Haotian Zhang, Fu |
| author_facet | Zhu, Fangcheng Ren, Yunfan Yin, Longji Kong, Fanze Liu, Qingbo Xue, Ruize Liu, Wenyi Cai, Yixi Lu, Guozheng Li, Haotian Zhang, Fu |
| contents | Aerial swarm systems possess immense potential in various aspects, such as cooperative exploration, target tracking, search and rescue. Efficient, accurate self and mutual state estimation are the critical preconditions for completing these swarm tasks, which remain challenging research topics. This paper proposes Swarm-LIO2: a fully decentralized, plug-and-play, computationally efficient, and bandwidth-efficient LiDAR-inertial odometry for aerial swarm systems. Swarm-LIO2 uses a decentralized, plug-and-play network as the communication infrastructure. Only bandwidth-efficient and low-dimensional information is exchanged, including identity, ego-state, mutual observation measurements, and global extrinsic transformations. To support the plug-and-play of new teammate participants, Swarm-LIO2 detects potential teammate UAVs and initializes the temporal offset and global extrinsic transformation all automatically. To enhance the initialization efficiency, novel reflectivity-based UAV detection, trajectory matching, and factor graph optimization methods are proposed. For state estimation, Swarm-LIO2 fuses LiDAR, IMU, and mutual observation measurements within an efficient ESIKF framework, with careful compensation of temporal delay and modeling of measurements to enhance the accuracy and consistency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17798 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Swarm-LIO2: Decentralized, Efficient LiDAR-inertial Odometry for UAV Swarms Zhu, Fangcheng Ren, Yunfan Yin, Longji Kong, Fanze Liu, Qingbo Xue, Ruize Liu, Wenyi Cai, Yixi Lu, Guozheng Li, Haotian Zhang, Fu Robotics Aerial swarm systems possess immense potential in various aspects, such as cooperative exploration, target tracking, search and rescue. Efficient, accurate self and mutual state estimation are the critical preconditions for completing these swarm tasks, which remain challenging research topics. This paper proposes Swarm-LIO2: a fully decentralized, plug-and-play, computationally efficient, and bandwidth-efficient LiDAR-inertial odometry for aerial swarm systems. Swarm-LIO2 uses a decentralized, plug-and-play network as the communication infrastructure. Only bandwidth-efficient and low-dimensional information is exchanged, including identity, ego-state, mutual observation measurements, and global extrinsic transformations. To support the plug-and-play of new teammate participants, Swarm-LIO2 detects potential teammate UAVs and initializes the temporal offset and global extrinsic transformation all automatically. To enhance the initialization efficiency, novel reflectivity-based UAV detection, trajectory matching, and factor graph optimization methods are proposed. For state estimation, Swarm-LIO2 fuses LiDAR, IMU, and mutual observation measurements within an efficient ESIKF framework, with careful compensation of temporal delay and modeling of measurements to enhance the accuracy and consistency. |
| title | Swarm-LIO2: Decentralized, Efficient LiDAR-inertial Odometry for UAV Swarms |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.17798 |