Visual Inertial Odometry

Visual-inertial odometry (VIO) is a technique for estimating the position and orientation of a moving agent by fusing data from cameras and inertial measurement units (IMUs). Current research emphasizes improving VIO robustness and accuracy in challenging environments (e.g., low-texture scenes, dynamic objects) through advancements in sensor fusion algorithms (like Extended Kalman Filters and tightly-coupled optimization), model architectures (including neural networks for noise reduction and online calibration), and the incorporation of additional sensor modalities (LiDAR, UWB). These improvements are crucial for enabling reliable autonomous navigation in robotics, augmented reality, and other applications where precise localization is essential.