Stereo Visual Odometry

Stereo visual odometry (SVO) aims to estimate a camera's 3D movement by analyzing stereo image pairs, crucial for robotics and autonomous navigation. Current research focuses on improving accuracy and robustness, particularly in challenging conditions like dynamic scenes, low light, and adverse weather, often employing techniques like deep learning for feature matching (e.g., using attention graph neural networks for point and line features) and integrating inertial measurement units (IMUs) to enhance pose estimation. These advancements are significant for improving the reliability of autonomous systems, enabling more precise localization and mapping in diverse and complex environments.