重新审视多GNSS无人机导航——一种等变滤波方法
Revisiting multi-GNSS Navigation for UAVs -- An Equivariant Filtering Approach
AI总结：
本文利用等变滤波（EqF）改进无人机多GNSS导航的状态估计，通过对称性方法解决传统MEKF的局限性，并在INSANE数据集上验证了其性能提升。
摘要：
在这项工作中，我们探索了等变滤波在惯性导航系统中的最新进展，以改进无人驾驶飞行器（UAV）的状态估计。传统的先进估计方法，如乘法卡尔曼滤波器（MEKF），在一致性、初始状态估计误差和收敛性能方面存在一些局限性。基于对称性的方法，如等变滤波器（EqF），通过利用系统的数学性质——其对称性，在这些方面提供了显著优势。这些滤波器通过其误差定义实现了更快的收敛速度和对错误初始状态估计的鲁棒性。为了展示EqF的实用性，我们专注于户外机器人中最常见传感器的传感器融合问题：全球导航卫星系统（GNSS）传感器和惯性测量单元（IMU）。我们提供了利用对称群的半直积来推导滤波器方程的EqF实现。为了验证EqF在现实场景中的实际可用性，我们使用INSANE数据集的所有户外运行数据评估了我们的方法。我们的结果证明了EqF在真实环境中的性能改进，突显了其增强无人机状态估计的潜力。
英文摘要：
In this work, we explore the recent advances in equivariant filtering for inertial navigation systems to improve state estimation for uncrewed aerial vehicles (UAVs). Traditional state-of-the-art estimation methods, e.g., the multiplicative Kalman filter (MEKF), have some limitations concerning their consistency, errors in the initial state estimate, and convergence performance. Symmetry-based methods, such as the equivariant filter (EqF), offer significant advantages for these points by exploiting the mathematical properties of the system - its symmetry. These filters yield faster convergence rates and robustness to wrong initial state estimates through their error definition. To demonstrate the usability of EqFs, we focus on the sensor-fusion problem with the most common sensors in outdoor robotics: global navigation satellite system (GNSS) sensors and an inertial measurement unit (IMU). We provide an implementation of such an EqF leveraging the semi-direct product of the symmetry group to derive the filter equations. To validate the practical usability of EqFs in real-world scenarios, we evaluate our method using data from all outdoor runs of the INSANE Dataset. Our results demonstrate the performance improvements of the EqF in real-world environments, highlighting its potential for enhancing state estimation for UAVs.
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。
