基于去中心化EKF的多机器人系统无GNSS协同定位算法中零速更新的收益评估
Evaluation of the Benefits of Zero Velocity Update in Decentralized EKF-Based Cooperative Localization Algorithms for GNSS-Denied Multi-Robot Systems
AI总结：
针对无GNSS多机器人系统定位问题，提出在基于DEKF的定位算法中协同使用零速更新，经仿真与真实实验验证，该方法可显著提升3D定位精度并帮助其他机器人恢复定位。
摘要：
本文提出在面向多机器人系统的基于去中心化扩展卡尔曼滤波（DEKF）的定位算法中，协同使用零速更新（ZU）。该滤波器利用惯性测量单元（IMU）、超宽带（UWB）以及里程计速度测量，提升系统在无GNSS环境下的定位性能。本研究的贡献在于评估在基于DEKF的定位算法中使用ZU的收益。该算法在视频动作捕捉设施的真实硬件上，以及面向无人地面车辆（UGV）的基于机器人操作系统（ROS）的仿真环境中进行了测试。仿真与真实世界实验均表明，在多机器人系统中，单个机器人使用ZU可恢复其他机器人的定位能力。来自无GNSS仿真与真实环境的实验结果显示，在DEKF中结合简单启发式规则使用ZU，可显著提升3D定位精度。
英文摘要：
This paper proposes the cooperative use of zero velocity update (ZU) in a decentralized extended Kalman filter (DEKF) based localization algorithm for multi-robot systems. The filter utilizes inertial measurement unit (IMU), ultra-wideband (UWB), and odometry velocity measurements to improve the localization performance of the system in the presence of a GNSS-denied environment. The contribution of this work is to evaluate the benefits of using ZU in a DEKF-based localization algorithm. The algorithm is tested with real hardware in a video motion capture facility and a Robot Operating System (ROS) based simulation environment for unmanned ground vehicles (UGV). Both simulation and real-world experiments are performed to show the effectiveness of using ZU in one robot to reinstate the localization of other robots in a multi-robot system. Experimental results from GNSS-denied simulation and real-world environments show that using ZU with simple heuristics in the DEKF significantly improves the 3D localization accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
