基于IMU和地标测量的视觉惯性导航无迹粒子滤波器
Unscented Particle Filter for Visual-inertial Navigation using IMU and Landmark Measurements
AI总结：
本文提出了一种基于四元数的无迹粒子滤波器QUPF-VIN，通过融合低成本IMU和视觉地标观测数据，专为GPS拒止环境下的6 DoF车辆设计，实验证明其跟踪精度优于标准Kalman滤波器。
摘要：
本文提出了一种几何上基于四元数的无迹粒子滤波器，用于视觉惯性导航（QUPF-VIN），专为六自由度（6 DoF）运行的车辆设计。所提出的QUPF-VIN技术基于四元数，捕捉了真实导航运动学的固有非线性特性。该滤波器融合了来自低成本惯性测量单元（IMU）的数据和通过视觉传感器获得的地标观测。QUPF-VIN以离散形式实现，以确保与机载惯性传感系统无缝集成。该方法专为在GPS拒止环境中保持鲁棒性而设计，已通过涉及配备6轴IMU和立体相机、以6 DoF运行的无人飞行器（UAV）的真实世界数据集实验进行了验证。数值结果表明，与真实地面数据相比，QUPF-VIN提供了卓越的跟踪精度。此外，与基于标准Kalman滤波器的导航技术进行比较分析，进一步突出了QUPF-VIN的增强性能。
英文摘要：
This paper introduces a geometric Quaternion-based Unscented Particle Filter for Visual-Inertial Navigation (QUPF-VIN) specifically designed for a vehicle operating with six degrees of freedom (6 DoF). The proposed QUPF-VIN technique is quaternion-based capturing the inherently nonlinear nature of true navigation kinematics. The filter fuses data from a low-cost inertial measurement unit (IMU) and landmark observations obtained via a vision sensor. The QUPF-VIN is implemented in discrete form to ensure seamless integration with onboard inertial sensing systems. Designed for robustness in GPS-denied environments, the proposed method has been validated through experiments with real-world dataset involving an unmanned aerial vehicle (UAV) equipped with a 6-axis IMU and a stereo camera, operating with 6 DoF. The numerical results demonstrate that the QUPF-VIN provides superior tracking accuracy compared to ground truth data. Additionally, a comparative analysis with a standard Kalman filter-based navigation technique further highlights the enhanced performance of the QUPF-VIN.
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。
