多机器人定位新框架，通信少、一致性高，接近中心化效果。
D-CLIPSE: Distributed Consensus-based Localization with Passive Listening on Shared State Exchange
- 通过共享预积分里程计和共享状态实现分布式共识定位
- 仿真与实验均达近中心化精度与一致性表现
- 适合资源受限的多机器人协同系统
多机器人精准且一致的定位对路径规划与控制等下游任务至关重要。集中式滤波方法虽能最优融合全队传感器数据，但受限于硬件、通信与计算能力，难以实际部署。分布式方法在每台机器人上运行滤波器，利用机器人间通信估计自身及邻居状态。本文提出一种一致、高效通信、基于共识的分布式滤波框架，通过共享预积分里程计与相关共享状态实现定位。该方法在仿真与实验中验证，相较于当前最先进去中心化方法，在精度与一致性方面均接近中心化性能。
原文摘要 · Abstract (English)
Multi-robot localization that is accurate and consistent is imperative for downstream tasks such as planning and control. Centralized filtering approaches optimally fuse all available sensor measurements of the team. However, a centralized solution is rarely implementable due to hardware, communication, and computational constraints. Distributed approaches deploy a filter on each robot to estimate their own state and neighbours' states using inter-robot communication. This paper proposes a consistent, communication-efficient, and consensus-based distributed filtering framework that shares both preintegrated odometry and relevant shared states among communicating robots. The proposed method is validated in simulated and experimental scenarios, showing near centralized performance in accuracy, and especially in consistency, compared to the current state-of-the-art decentralized approach.
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