Visual Inertial Navigation

Visual-inertial navigation (VIN) integrates data from cameras and inertial measurement units (IMUs) to estimate a system's pose (position and orientation), aiming for robust and accurate localization, especially in GPS-denied environments. Current research emphasizes improving accuracy and efficiency through advanced calibration techniques for multiple IMUs, novel filter designs (e.g., using Schur complements or manifold optimization), and the integration of neural radiance fields (NeRFs) for enhanced pose estimation and uncertainty quantification. These advancements are crucial for applications in robotics, autonomous vehicles, and other areas requiring precise and reliable localization in challenging conditions.