SLAM Algorithm

Simultaneous Localization and Mapping (SLAM) algorithms aim to build a map of an environment while simultaneously tracking a robot's or sensor's location within that map. Current research focuses on improving robustness and accuracy, particularly through advancements in neural network-based approaches like NeRF-SLAM, which leverage implicit scene representations, and the integration of diverse sensor data, including lidar, inertial measurement units, and even event cameras. These improvements are crucial for enabling reliable autonomous navigation in challenging environments, with applications ranging from robotics and autonomous vehicles to augmented reality and 3D modeling.