Navigation Satellite System Visual Inertial

Navigation satellite system visual-inertial (GNSS-VIO) systems aim to achieve robust and accurate localization by fusing data from GNSS, inertial measurement units (IMUs), and cameras. Current research emphasizes tightly coupled integration of diverse GNSS measurements (pseudorange, Doppler, carrier phase) with visual data, often employing efficient filtering techniques like square-root inverse filters or factor graph optimization within a sliding window framework. This fusion improves accuracy and robustness, particularly in challenging environments like urban canyons or GNSS-denied scenarios, where algorithms leverage techniques such as sky segmentation or inertial priors to mitigate drift. The resulting advancements have significant implications for autonomous navigation in various applications, including robotics, unmanned aerial vehicles (UAVs), and precision agriculture.