Metric Semantic SLAM

Metric semantic SLAM integrates geometric mapping (SLAM) with semantic understanding of the environment, aiming to create accurate, labeled 3D models. Current research emphasizes robust methods for fusing data from diverse sensors (LiDAR, cameras, IMUs) to handle dynamic environments and improve accuracy, often employing deep learning for semantic segmentation and sophisticated algorithms like pose graph optimization and graduated non-convexity for outlier rejection. This technology is crucial for autonomous robots operating in complex, unstructured settings, with applications ranging from precision agriculture and infrastructure inspection to human-robot interaction.