RGB D Slam

RGB-D SLAM (Simultaneous Localization and Mapping) aims to build accurate 3D models of environments using data from RGB and depth cameras, simultaneously determining the camera's position and orientation. Current research focuses on improving the accuracy and efficiency of these systems, exploring techniques like Gaussian splatting for dense 3D reconstruction, implicit neural representations for efficient mapping, and hybrid approaches combining feature-based and dense methods to mitigate limitations of each. These advancements are crucial for robotics, augmented reality, and other applications requiring robust real-time 3D scene understanding in dynamic environments.