提出混合滤波器，提升无人机在复杂环境下的定位精度与稳定性。
Adaptive Covariance and Quaternion-Focused Hybrid Error-State EKF/UKF for Visual-Inertial Odometry
- 分步融合：先用扩展卡尔曼滤波传播状态，再用无迹滤波优化姿态。
- 挑战场景下位置误差降低49%，旋转精度比传统方法高57%。
- 计算量仅需全无迹滤波的一半，适合实时无人机系统。
本研究针对无人机（UAV）提出一种新型混合视觉-惯性里程计（VIO）方法，具备应对环境挑战的能力并可动态评估传感器可靠性。基于松耦合传感器融合架构，系统采用创新的面向四元数的误差状态扩展卡尔曼滤波/无迹卡尔曼滤波（Qf-ES-EKF/UKF）结构处理惯性测量单元（IMU）数据：首先使用误差状态扩展卡尔曼滤波（ESKF）传播完整状态，随后通过针对性的缩放无迹卡尔曼滤波（SUKF）步骤仅对方向进行精修。该流程结合了SUKF在四元数估计中的高精度与ESKF的整体低计算开销优势。视觉测量可靠性通过动态传感器置信度评分评估，依据图像熵、亮度变化、运动模糊程度及推理质量等指标调整观测噪声协方差，确保在恶劣条件下仍能实现稳定位姿估计。在EuRoC MAV数据集上的全面实验表明：在挑战性场景下平均位置精度提升49%，旋转精度较基于ESKF的方法平均提高57%，且达到与SUKF相当的精度，但计算成本比完整SUKF实现降低约48%。结果证明该方法在计算效率与估计精度间取得良好平衡，显著提升了复杂环境中传感器可靠性波动下的无人机位姿估计性能。
原文摘要 · Abstract (English)
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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