SLAM Pipeline

Simultaneous Localization and Mapping (SLAM) pipelines aim to build accurate maps of an environment while simultaneously tracking a robot's or sensor's location within that environment. Current research emphasizes improving robustness and efficiency through diverse approaches, including integrating deep learning for feature extraction and scene understanding, leveraging inverse imaging models to mitigate sensor noise, and developing novel algorithms for handling dynamic environments and various sensor modalities (e.g., LiDAR, radar, RGB-D cameras). These advancements are crucial for enabling reliable autonomous navigation in robotics, augmented reality, and other applications requiring real-time 3D scene reconstruction.