In-Flight Initialization of Global Visual-Inertial Estimators Using Geospatial Data

In this work, we propose a solution that leverages geospatial data to initialize the monocular visual-inertial nav- igation system. For Visual-Inertial Navigation Systems (VINS) operating on UAVs, the ability to perform initialization and re- localization in mid-air is essential. However, degenerate motion can cause VINS to lose scale, making traditional initialization algorithms less reliable. To address this issue, we fuse geo- graphic information in the initialization process, and utilize a learning-based feature matching algorithm to associate the information with inertial states. The proposed approach demon- strates adaptability to the degenerate motions of UAVs and significantly surpasses the estimation accuracy of conventional VINS initialization algorithms. Compared to methods that assist initialization by using a laser-range-finder (LRF), the proposed method solely relies on low-cost satellite imagery and elevation information. We evaluate the proposed approach on a large- scale UAV dataset, and compare with existing methods. The results demonstrate the superior effectiveness of the proposed method.