Xvins: Boosting State Estimation Robustness via Hybrid Temporal Tracking and Efficient Deep Feature Extraction

Abstract

Robust state estimation in GPS-denied environments remains a primary challenge for Visual-Inertial Odometry (VIO). Traditional VIO pipelines relying on hand-crafted features often experience drift or tracking failure under rapid motion and dynamic lighting. While deep learning-based local features offer improved robustness, high computational costs frequently limit their adoption in real-time, resource-constrained systems. In this work, we propose XVINS, a hybrid VIO frontend integrating XFeat—a lightweight deep feature extractor—into the optimization-based VINS-Fusion framework. The system employs a dual-strategy tracking mechanism: computationally efficient KLT optical flow is utilized for high-frequency temporal tracking, while XFeat in-ference is dynamically triggered for feature replenishment during tracking degradation. We evaluate this architecture across the 11 sequences of the EuRoC MAV dataset. The results indicate improved robustness to motion blur, reducing absolute trajectory error by up to 82.7% in highly dynamic scenarios compared to baseline methods. Furthermore, we analyze the theoretical and practical tradeoffs between deep feature quantization and classical sub-pixel precision, presenting XVINS as a viable, real-time state estimation solution for agile Micro-Aerial Vehicles (MAVs) and mobile platforms.