多机器人SE(2)相对位姿估计的可观测性分析：扩展卡尔曼滤波与鲁棒位姿图优化的比较
Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization
AI总结：
本研究针对多机器人相对位姿估计的可观测性，比较EKF与鲁棒PGO在仿真和硬件实验中的精度，提出不同感知通信结构下的估计方案。
摘要：
在本研究中，我们解决了多机器人定位问题，特别关注协作定位和相对位姿估计的可观测性分析。协作定位涉及通过通信网络和消息传递增强每个机器人的信息。如果目标机器人的里程计数据能够传输给自机器人，则通过仅距离或仅方位测量即可实现其相对位姿估计的可观测性，前提是两个机器人均具有非零线速度。在目标机器人的里程计数据不直接传输而由自机器人估计的情况下，需要同时使用距离和方位测量来确保相对位姿估计的可观测性。对于ROS/Gazebo仿真，我们探索了四种感知和通信结构。我们比较了使用不同鲁棒损失函数（不同滑动窗口批大小的滤波和平滑）的扩展卡尔曼滤波（EKF）和位姿图优化（PGO）在估计精度方面的表现。在硬件实验中，使用两个配备UWB模块的Turtlebot3进行真实世界机器人间相对位姿估计，同时应用EKF和PGO并比较其性能。
英文摘要：
In this study, we address multi-robot localization issues, with a specific focus on cooperative localization and observability analysis of relative pose estimation. Cooperative localization involves enhancing each robot's information through a communication network and message passing. If odometry data from a target robot can be transmitted to the ego robot, observability of their relative pose estimation can be achieved through range-only or bearing-only measurements, provided both robots have non-zero linear velocities. In cases where odometry data from a target robot are not directly transmitted but estimated by the ego robot, both range and bearing measurements are necessary to ensure observability of relative pose estimation. For ROS/Gazebo simulations, we explore four sensing and communication structures. We compare extended Kalman filtering (EKF) and pose graph optimization (PGO) estimation using different robust loss functions (filtering and smoothing with varying batch sizes of sliding windows) in terms of estimation accuracy. In hardware experiments, two Turtlebot3 equipped with UWB modules are used for real-world inter-robot relative pose estimation, applying both EKF and PGO and comparing their performance.
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