Visual Inertial Initialization

Visual-inertial initialization aims to accurately estimate the initial pose and sensor parameters (e.g., biases, gravity) of a visual-inertial system, crucial for robust and accurate subsequent pose tracking. Current research focuses on improving initialization accuracy and robustness, particularly in challenging scenarios with limited motion or poor visual features, employing techniques like bundle adjustment, error-state Kalman filtering, and incorporating learned depth priors to constrain the optimization process. These advancements are significant for applications such as augmented reality, robotics, and autonomous navigation, enabling more reliable and precise pose estimation in diverse environments.