Object SLAM

Object SLAM aims to simultaneously build a map of the environment and track a robot's location within it, using objects as key features rather than solely relying on points or lines. Current research focuses on improving robustness to noise and environmental changes, often employing neural networks (e.g., neural radiance fields) and advanced data association techniques to handle object symmetries and ambiguities. These advancements are crucial for enhancing the reliability and scalability of robots operating in dynamic, unstructured environments, with applications ranging from autonomous driving to augmented reality.