DeepUKF-VIN：基于IMU-Vision-Net的自适应调节深度无迹卡尔曼滤波器用于三维视觉惯性导航
DeepUKF-VIN: Adaptively-tuned Deep Unscented Kalman Filter for 3D Visual-Inertial Navigation based on IMU-Vision-Net
AI总结：
本文提出DeepUKF-VIN，利用IMU-Vision-Net和深度学习自适应机制调节无迹卡尔曼滤波器噪声协方差，实现GPS拒止环境下三维视觉惯性导航的方位、位置和速度鲁棒估计，并在真实数据上验证其优于标准UKF。
摘要：
本文解决了在三维（3D）空间中具有六自由度（6-DoF）的载具的方位、位置和速度估计的挑战。提出了一种基于深度学习的自适应机制（DLAM），利用IMU-Vision-Net自适应调节卡尔曼型滤波器在视觉惯性导航（VIN）问题中的噪声协方差矩阵。随后，引入了一种用于三维VIN的自适应调节深度学习无迹卡尔曼滤波器（DeepUKF-VIN），以利用所提出的DLAM，从而稳健地估计关键导航分量，包括方位、位置和线速度。所提出的DeepUKF-VIN融合了机载传感器数据，具体包括惯性测量单元（IMU）和从相机提取的视觉特征点，适用于GPS拒止环境下的导航。其基于四元数的设计有效捕捉导航非线性，并避免了基于欧拉角的滤波器通常遇到的奇异性问题。DeepUKF-VIN在离散空间中实现，便于实际滤波器部署。使用从IMU和立体相机以低采样率采集的真实世界数据评估了该滤波器的性能。结果表明滤波器具有稳定性，估计误差快速衰减，突出了其高估计精度。此外，在两种场景下与标准无迹卡尔曼滤波器（UKF）进行的对比测试一致显示在所有导航分量上均具有更优性能，从而验证了所提出的DeepUKF-VIN的有效性和鲁棒性。关键词：深度学习，无迹卡尔曼滤波器，自适应调节，估计，导航，无人飞行器，传感器融合。
英文摘要：
This paper addresses the challenge of estimating the orientation, position, and velocity of a vehicle operating in three-dimensional (3D) space with six degrees of freedom (6-DoF). A Deep Learning-based Adaptation Mechanism (DLAM) is proposed to adaptively tune the noise covariance matrices of Kalman-type filters for the Visual-Inertial Navigation (VIN) problem, leveraging IMU-Vision-Net. Subsequently, an adaptively tuned Deep Learning Unscented Kalman Filter for 3D VIN (DeepUKF-VIN) is introduced to utilize the proposed DLAM, thereby robustly estimating key navigation components, including orientation, position, and linear velocity. The proposed DeepUKF-VIN integrates data from onboard sensors, specifically an inertial measurement unit (IMU) and visual feature points extracted from a camera, and is applicable for GPS-denied navigation. Its quaternion-based design effectively captures navigation nonlinearities and avoids the singularities commonly encountered with Euler-angle-based filters. Implemented in discrete space, the DeepUKF-VIN facilitates practical filter deployment. The filter's performance is evaluated using real-world data collected from an IMU and a stereo camera at low sampling rates. The results demonstrate filter stability and rapid attenuation of estimation errors, highlighting its high estimation accuracy. Furthermore, comparative testing against the standard Unscented Kalman Filter (UKF) in two scenarios consistently shows superior performance across all navigation components, thereby validating the efficacy and robustness of the proposed DeepUKF-VIN. Keywords: Deep Learning, Unscented Kalman Filter, Adaptive tuning, Estimation, Navigation, Unmanned Aerial Vehicle, Sensor-fusion.
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