Monocular Visual Odometry

Monocular visual odometry (VO) aims to estimate a camera's movement using images from a single camera, a crucial task for autonomous navigation and augmented reality. Current research focuses on improving robustness and accuracy, particularly addressing challenges like scale ambiguity and drift, through techniques such as incorporating depth information (learned or encoded), employing deep learning architectures (including transformers and recurrent networks), and integrating multiple visual-inertial odometry modules. These advancements are significant for applications requiring precise localization in GPS-denied environments, enhancing the capabilities of robots, drones, and other autonomous systems.