PIEKF-VIWO: Visual-Inertial-Wheel Odometry using Partial Invariant Extended Kalman Filter

TL;DR

This paper introduces PIEKF-VIWO, a novel visual-inertial-wheel odometry method using a partial invariant extended Kalman filter that improves accuracy and consistency by incorporating rotation-velocity states and kinematic constraints.

Contribution

The paper proposes a partial IEKF for VIWO that simplifies wheel odometry integration and enhances positioning accuracy through novel measurement models and constraints.

Findings

Outperforms standard MSCKF in accuracy and consistency

Effective in real-world and simulated environments

Utilizes a dynamic outlier detection method

Abstract

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.