Joint Localization

Joint localization, the simultaneous estimation of multiple related entities' positions, is a core problem across diverse fields like robotics, computer vision, and signal processing. Current research focuses on improving accuracy and robustness using various approaches, including diffusion models for end-to-end navigation and planning, hierarchical relation networks for multi-person pose estimation, and optimal transport formulations for handling data association challenges in multi-source localization. These advancements are crucial for enhancing applications such as autonomous navigation, human-computer interaction, and medical image analysis, where precise and efficient localization of multiple objects or features is essential.