Inertial Navigation System

Inertial Navigation Systems (INS) estimate position, velocity, and orientation using accelerometers and gyroscopes, crucial for autonomous navigation when GPS is unavailable. Current research heavily focuses on improving INS accuracy by integrating complementary sensor data (e.g., radar, LiDAR, cameras, acoustic systems) and employing advanced filtering techniques like Kalman filters (including Extended and Unscented variants), factor graph optimization, and equivariant filters, often coupled with deep learning for tasks like bias compensation and outlier rejection. These advancements are significantly impacting various fields, including robotics, autonomous vehicles, and UAV navigation, by enabling more robust and precise localization in challenging environments.