基于四元数的无迹卡尔曼滤波器用于GPS拒止区域的6自由度视觉惯性导航
Quaternion-based Unscented Kalman Filter for 6-DoF Vision-based Inertial Navigation in GPS-denied Regions
AI总结：
针对GPS拒止区域刚体6自由度运动的位姿、位置和线速度估计问题，提出定义在特定空间的计算高效的四元数导航无迹卡尔曼滤波器，融合视觉与IMU数据，经真实数据集验证其鲁棒性和有效性。
摘要：
本文研究了在三维（3D）空间中以六自由度（6 DoF）运动的刚体的姿态、位置和线速度估计问题。为确保导航问题的全局表示，本文构建了高度非线性的导航运动学模型。本文提出了一种计算高效的基于四元数的导航无迹卡尔曼滤波器（QNUKF），其定义在$\mathbb{S}^{3}\times\mathbb{R}^{3}\times\mathbb{R}^{3}$空间上，模拟真实的非线性导航运动学，并利用机载视觉惯性导航（VIN）单元实现GPS拒止环境下的成功导航。所提出的QNUKF采用离散形式设计，基于视觉单元（立体或单目相机）获取的图像数据与低成本惯性测量单元（IMU）采集的信息进行数据融合。图像数据用于提取3D空间中的特征点，而6轴IMU提供相对于机体坐标系的角速度和加速度计测量值。通过在无人机采集的真实世界数据集（包含立体图像和6轴IMU测量值）上进行实验，验证了所提出QNUKF的鲁棒性和有效性；同时，该方法还与标准的最先进滤波技术进行了对比验证。
英文摘要：
This paper investigates the orientation, position, and linear velocity estimation problem of a rigid-body moving in three-dimensional (3D) space with six degrees-of-freedom (6 DoF). The highly nonlinear navigation kinematics are formulated to ensure global representation of the navigation problem. A computationally efficient Quaternion-based Navigation Unscented Kalman Filter (QNUKF) is proposed on $\mathbb{S}^{3}\times\mathbb{R}^{3}\times\mathbb{R}^{3}$ imitating the true nonlinear navigation kinematics and utilize onboard Visual-Inertial Navigation (VIN) units to achieve successful GPS-denied navigation. The proposed QNUKF is designed in discrete form to operate based on the data fusion of photographs garnered by a vision unit (stereo or monocular camera) and information collected by a low-cost inertial measurement unit (IMU). The photographs are processed to extract feature points in 3D space, while the 6-axis IMU supplies angular velocity and accelerometer measurements expressed with respect to the body-frame. Robustness and effectiveness of the proposed QNUKF have been confirmed through experiments on a real-world dataset collected by a drone navigating in 3D and consisting of stereo images and 6-axis IMU measurements. Also, the proposed approach is validated against standard state-of-the-art filtering techniques. IEEE Keywords: Localization, Navigation, Unmanned Aerial Vehicle, Sensor-fusion, Inertial Measurement Unit, Vision Unit.
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。
