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.

翻译：本文提出在基于分散式扩展卡尔曼滤波器（DEKF）的多机器人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）及里程计速度测量值，在GNSS拒止环境下提升系统定位性能。本工作的贡献在于评估在DEKF定位算法中使用ZU的收益。算法在视频运动捕捉设施的真实硬件平台以及基于机器人操作系统（ROS）的地面无人车辆（UGV）仿真环境中进行测试。通过仿真与真实世界实验，证明了在单个机器人上使用ZU可恢复多机器人系统中其他机器人定位能力的有效性。GNSS拒止仿真环境及真实场景的实验结果表明，在DEKF中结合简单启发式规则使用ZU，可显著提升三维定位精度。