Efficient Slam

Efficient Simultaneous Localization and Mapping (SLAM) focuses on developing faster and more robust methods for robots and other systems to simultaneously build a map of their environment and determine their location within it. Current research emphasizes integrating diverse sensor modalities (e.g., RGB-D cameras, lidar, event cameras) and employing advanced algorithms like Gaussian splatting, differentiable ICP variants, and deep learning for feature extraction and pose estimation to improve accuracy and speed. These advancements are crucial for applications such as robotic surgery, autonomous navigation, and augmented reality, where real-time performance and reliable mapping in dynamic environments are essential.