Inertial Navigation

Inertial navigation uses inertial sensors (accelerometers and gyroscopes) to estimate a vehicle's position and orientation, primarily aiming to overcome limitations of GPS-denied environments or enhance its accuracy. Current research emphasizes improving the robustness and accuracy of inertial navigation through advanced filtering techniques (e.g., Kalman filters, unscented Kalman filters on manifolds), data-driven approaches like deep learning (e.g., recurrent neural networks, transformer networks), and innovative sensor fusion strategies incorporating data from other sensors (e.g., UWB, lidar, magnetometers). These advancements are crucial for applications ranging from autonomous vehicles and robotics to pedestrian navigation and unmanned aerial vehicles, enabling more reliable and precise localization in diverse and challenging conditions.