  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.
