SLAM Method

Simultaneous Localization and Mapping (SLAM) is a computational problem focusing on building a map of an unknown environment while simultaneously tracking the robot's location within that map. Current research emphasizes improving SLAM's accuracy and robustness in challenging conditions, such as underwater or underground environments, and for resource-constrained robots, leading to the development of novel algorithms like those incorporating soft Manhattan world constraints and Gaussian splatting. This active area of research has significant implications for robotics, autonomous navigation, and 3D scene reconstruction, impacting applications ranging from assistive robots to autonomous vehicles.