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

TL;DR

SP-VIO introduces a robust, efficient filter-based visual inertial odometry method that improves accuracy and stability by novel state transformations and pose-only visual descriptions, especially under visual deprivation.

Contribution

The paper proposes the DST-EKF and pose-only visual description to enhance filter-based VIO accuracy, consistency, and robustness in resource-constrained environments.

Findings

SP-VIO outperforms state-of-the-art VIO algorithms in accuracy and efficiency.

The method maintains better robustness under visual deprived conditions.

Enhanced trajectory optimization during visual interruptions.

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.