自适应协方差与四元数聚焦混合误差状态EKF/UKF用于视觉-惯性里程计
Adaptive Covariance and Quaternion-Focused Hybrid Error-State EKF/UKF for Visual-Inertial Odometry
AI总结：
本文提出一种自适应协方差与四元数聚焦混合误差状态EKF/UKF方法，用于提升无人机在复杂环境下的视觉-惯性里程计性能，实现高精度姿态估计与低计算成本的平衡。
摘要：
本研究提出了一种创新的混合视觉-惯性里程计（VIO）方法，适用于无人驾驶飞行器（UAVs），能够抵御环境挑战，并能够动态评估传感器可靠性。该系统基于松耦合传感器融合架构，利用一种新颖的四元数聚焦误差状态扩展卡尔曼滤波器/无迹卡尔曼滤波器（Qf-ES-EKF/UKF）架构来处理惯性测量单元（IMU）数据。该架构首先使用误差状态扩展卡尔曼滤波器（ESKF）传播整个状态，然后应用一个针对的缩放无迹卡尔曼滤波器（SUKF）步骤来仅细化方向。这种顺序过程将SUKF在四元数估计中的准确性与ESKF的整体计算效率相结合。通过基于图像熵、强度变化、运动模糊和推理质量等指标的动态传感器置信度分数来评估视觉测量的可靠性，将测量噪声协方差适应化，以确保在挑战性条件下也能稳定地估计姿态。对EuRoC MAV数据集的全面实验分析显示了关键优势：在挑战性场景中位置精度平均提高49%，在ESKF基础上的方法中旋转精度平均提高57%，并且与SUKF相比，计算成本降低了约48%。这些发现表明，所提出的方法在计算效率和估计精度之间取得了有效的平衡，并显著提高了在传感器可靠性变化的复杂环境中UAV姿态估计性能。
英文摘要：
This study presents an innovative hybrid Visual-Inertial Odometry (VIO) method for Unmanned Aerial Vehicles (UAVs) that is resilient to environmental challenges and capable of dynamically assessing sensor reliability. Built upon a loosely coupled sensor fusion architecture, the system utilizes a novel hybrid Quaternion-focused Error-State EKF/UKF (Qf-ES-EKF/UKF) architecture to process inertial measurement unit (IMU) data. This architecture first propagates the entire state using an Error-State Extended Kalman Filter (ESKF) and then applies a targeted Scaled Unscented Kalman Filter (SUKF) step to refine only the orientation. This sequential process blends the accuracy of SUKF in quaternion estimation with the overall computational efficiency of ESKF. The reliability of visual measurements is assessed via a dynamic sensor confidence score based on metrics, such as image entropy, intensity variation, motion blur, and inference quality, adapting the measurement noise covariance to ensure stable pose estimation even under challenging conditions. Comprehensive experimental analyses on the EuRoC MAV dataset demonstrate key advantages: an average improvement of 49% in position accuracy in challenging scenarios, an average of 57% in rotation accuracy over ESKF-based methods, and SUKF-comparable accuracy achieved with approximately 48% lower computational cost than a full SUKF implementation. These findings demonstrate that the presented approach strikes an effective balance between computational efficiency and estimation accuracy, and significantly enhances UAV pose estimation performance in complex environments with varying sensor reliability.
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