Localization Focus
Localization focus in current research centers on accurately determining the position and orientation of objects or agents within various environments, ranging from robotic navigation to medical image analysis and multimedia forensics. Key research areas employ deep learning models, including convolutional neural networks, transformers, and graph neural networks, often combined with probabilistic methods and optimization techniques like Bayesian optimization or ADMM to improve accuracy and efficiency. These advancements are crucial for improving autonomous systems, enhancing medical diagnostics, and combating the spread of misinformation through advanced forgery detection and localization capabilities. The development of robust and efficient localization methods has significant implications across diverse scientific disciplines and practical applications.
Papers
Cluster-based Method for Eavesdropping Identification and Localization in Optical Links
Haokun Song, Rui Lin, Andrea Sgambelluri, Filippo Cugini, Yajie Li, Jie Zhang, Paolo Monti
Invariant Smoothing for Localization: Including the IMU Biases
Paul Chauchat (AMU, LIS), Silvère Bonnabel (CAOR), Axel Barrau (CAOR)