多机器人协同定位新算法，通信少、精度高且抗干扰。
Distributed Invariant Kalman Filter for Object-level Multi-robot Pose SLAM
- 基于协方差交集的分布式不变卡尔曼滤波，提升状态估计稳定性。
- 利用物体级观测模型，通信开销降低，适合大规模系统。
- 可容忍部分机器人退化，适合真实复杂场景应用。
多机器人协同定位与目标跟踪对实现高级任务至关重要。本文提出一种基于协方差交集的分布式不变卡尔曼滤波，用于高效多机器人位姿估计。该方法采用物体级测量模型，进一步压缩信息，降低通信负担；通过在特殊李群上建模状态，增强不变卡尔曼滤波结构的线性性与一致性。结合协方差交集（CI）与卡尔曼滤波（KF），避免在复杂未知相关性下产生过度自信或保守估计，且允许一定程度的机器人性能退化，仍可通过协作保持整体精度。仿真与真实数据实验验证了该算法的可行性与优越性。
原文摘要 · Abstract (English)
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.
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