Invariant Extended Kalman Filter (IEKF) has been successfully applied in Visual-inertial Odometry (VIO) as an advanced achievement of Kalman filter, showing great potential in sensor fusion. In this paper, we propose partial IEKF (PIEKF), which only incorporates rotation-velocity state into the Lie group structure and apply it for Visual-Inertial-Wheel Odometry (VIWO) to improve positioning accuracy and consistency. Specifically, we derive the rotation-velocity measurement model, which combines wheel measurements with kinematic constraints. The model circumvents the wheel odometer's 3D integration and covariance propagation, which is essential for filter consistency. And a plane constraint is also introduced to enhance the position accuracy. A dynamic outlier detection method is adopted, leveraging the velocity state output. Through the simulation and real-world test, we validate the effectiveness of our approach, which outperforms the standard Multi-State Constraint Kalman Filter (MSCKF) based VIWO in consistency and accuracy.

翻译：不变扩展卡尔曼滤波（IEKF）作为卡尔曼滤波的先进成果，已成功应用于视觉-惯性里程计（VIO），在传感器融合中展现出巨大潜力。本文提出部分不变扩展卡尔曼滤波（PIEKF），仅将旋转-速度状态融入李群结构，并将其应用于视觉-惯性-轮式里程计（VIWO），以提升定位精度与一致性。具体而言，我们推导了融合轮式测量与运动学约束的旋转-速度测量模型，该模型避免了轮式里程计的三维积分与协方差传播过程，这对滤波器一致性至关重要。同时引入平面约束以增强位置精度，并采用利用速度状态输出的动态异常点检测方法。通过仿真与实测验证，本文方法在一致性与精度上均优于基于标准多状态约束卡尔曼滤波（MSCKF）的VIWO方案。