Landmark Based SLAM

Landmark-based Simultaneous Localization and Mapping (SLAM) focuses on building maps and tracking a robot's position using identifiable landmarks in the environment. Current research emphasizes robust methods for landmark detection and association, often employing deep learning models like YOLO for recognition and algorithms such as Extended Kalman Filters or graph-theoretic approaches for pose estimation and map optimization. This approach is particularly valuable in GPS-denied environments, such as battlefields or indoor spaces, and has applications in robotics, autonomous navigation, and medical image analysis, where accurate localization and map creation are crucial. The development of efficient and globally optimal solutions, along with improved data association techniques, remains a key focus.