Incremental Mapping

Incremental mapping focuses on efficiently building and updating maps of environments, particularly large-scale or dynamic ones, by adding new data incrementally rather than processing everything at once. Current research emphasizes robust and efficient algorithms, often employing techniques like Gaussian Mixture Models (GMMs), hierarchical structures (e.g., octrees), and Wasserstein distance-based keyframe selection to manage data redundancy and computational cost. This field is crucial for advancing robotics, autonomous navigation, and remote sensing applications, enabling more accurate and resource-efficient 3D reconstruction and localization in diverse settings.