Distributed Invariant Kalman Filter for Object-level Multi-robot Pose SLAM
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916394177658880 |
|---|---|
| author | Li, Haoying Zeng, Qingcheng Li, Haoran Zhang, Yanglin Wu, Junfeng |
| author_facet | Li, Haoying Zeng, Qingcheng Li, Haoran Zhang, Yanglin Wu, Junfeng |
| contents | Cooperative localization and target tracking are essential for multi-robot systems to implement high-level tasks. To this end, we propose a distributed invariant Kalman filter based on covariance intersection for effective multi-robot pose estimation. The paper utilizes the object-level measurement models, which have condensed information further reducing the communication burden. Besides, by modeling states on special Lie groups, the better linearity and consistency of the invariant Kalman filter structure can be stressed. We also use a combination of CI and KF to avoid overly confident or conservative estimates in multi-robot systems with intricate and unknown correlations, and some level of robot degradation is acceptable through multi-robot collaboration. The simulation and real data experiment validate the practicability and superiority of the proposed algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09410 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distributed Invariant Kalman Filter for Object-level Multi-robot Pose SLAM Li, Haoying Zeng, Qingcheng Li, Haoran Zhang, Yanglin Wu, Junfeng Robotics Cooperative localization and target tracking are essential for multi-robot systems to implement high-level tasks. To this end, we propose a distributed invariant Kalman filter based on covariance intersection for effective multi-robot pose estimation. The paper utilizes the object-level measurement models, which have condensed information further reducing the communication burden. Besides, by modeling states on special Lie groups, the better linearity and consistency of the invariant Kalman filter structure can be stressed. We also use a combination of CI and KF to avoid overly confident or conservative estimates in multi-robot systems with intricate and unknown correlations, and some level of robot degradation is acceptable through multi-robot collaboration. The simulation and real data experiment validate the practicability and superiority of the proposed algorithm. |
| title | Distributed Invariant Kalman Filter for Object-level Multi-robot Pose SLAM |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.09410 |