Dynamic SLAM

Dynamic SLAM aims to create accurate maps and robot localization in environments containing moving objects, a significant challenge for traditional SLAM methods. Current research focuses on developing robust methods for distinguishing between static and dynamic elements within the scene, often employing techniques like optical flow analysis, semantic segmentation, and neural radiance fields to achieve this separation. These advancements leverage probabilistic approaches, refined pose optimization algorithms, and novel motion decomposition strategies to improve accuracy and real-time performance. The resulting improvements in dynamic SLAM have broad implications for robotics, autonomous navigation, and augmented reality applications requiring reliable scene understanding in dynamic settings.