Hierarchical Localization

Hierarchical localization aims to efficiently and accurately determine the location of a system (e.g., robot, drone) within a large environment by breaking the problem into coarse and fine localization stages. Current research focuses on improving the robustness and efficiency of these pipelines, employing convolutional neural networks (CNNs), particularly those leveraging triplet loss functions or contrastive learning, and exploring various feature extraction and matching techniques, including the use of 3D meshes and adaptive image retrieval strategies. These advancements are significant for applications requiring real-time localization in challenging conditions, such as autonomous navigation and augmented reality, by reducing computational costs and improving accuracy.