Inertia Drift

Inertia drift, encompassing both the undesirable accumulation of errors in inertial navigation systems and the intentional execution of controlled drifts in dynamic systems like autonomous vehicles, is a subject of active research. Current efforts focus on mitigating drift through advanced regularization techniques in image processing and navigation, employing methods like $L_p$-norm regularization and deep learning-based unrolling networks, as well as developing sophisticated planning and control algorithms for precise, consecutive drifts in robotics. These advancements have implications for improving the accuracy and reliability of inertial navigation across various applications, and for enabling more agile and complex maneuvers in autonomous systems.