SP-VIO: Robust and Efficient Filter-Based Visual Inertial Odometry with State Transformation Model and Pose-Only Visual Description

Abstract

Due to the advantages of high computational efficiency and small memory requirements, filter-based visual inertial odometry (VIO) has a good application prospect in miniaturized and payload-constrained embedded systems. However, the filter-based method has the problem of insufficient accuracy. To this end, we propose the State transformation and Pose-only VIO (SP-VIO) by rebuilding the state and measurement models, and considering further visual deprived conditions. In detail, we first proposed the double state transformation extended Kalman filter (DST-EKF) to replace the standard extended Kalman filter (Std-EKF) for improving the system's consistency, and then adopt pose-only (PO) visual description to avoid the linearization error caused by 3D feature estimation. The comprehensive observability analysis shows that SP-VIO has a more stable unobservable subspace, which can better avoid the inconsistency problem caused by spurious information. Moreover, we propose an enhanced double state transformation Rauch-Tung-Striebel (DST-RTS) backtracking method to optimize motion trajectories during visual interruption. Monte-Carlo simulations and real-world experiments show that SP-VIO has better accuracy and efficiency than state-of-the-art (SOTA) VIO algorithms, and has better robustness under visual deprived conditions.