Long Term Visual Localization

Long-term visual localization aims to accurately determine a camera's position within a previously-visited environment, even across significant temporal gaps causing substantial environmental changes. Current research focuses on improving robustness to these changes by incorporating additional sensor data (e.g., GPS, IMU), developing efficient map representations (e.g., semantic maps, sparse maps generated via graph neural networks), and employing advanced feature learning techniques (including self-supervised methods) for more reliable image matching and pose estimation. These advancements are crucial for enabling reliable autonomous navigation in dynamic environments, with applications ranging from autonomous driving and robotics to augmented reality and underwater exploration.