Gaussian SLAM

Gaussian SLAM (Simultaneous Localization and Mapping) aims to build accurate and efficient 3D environment models using Gaussian distributions to represent uncertainty in robot pose and map features. Current research focuses on improving the efficiency and completeness of these models, employing techniques like Gaussian splatting for compact scene representation and incorporating prior knowledge, such as the Manhattan World assumption, to handle challenging environments. These advancements enable real-time 3D reconstruction at scale, with applications in robotics, augmented reality, and autonomous navigation, improving the accuracy and robustness of these systems in complex scenarios.