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
A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model Uncertainties
A. Noroozi, M. Ravan, B. Razavi, R. S. Fisher, Y. Law, M. S. Hasan
HPPS: A Hierarchical Progressive Perception System for Luggage Trolley Detection and Localization at Airports
Zhirui Sun, Zhe Zhang, Jieting Zhao, Hanjing Ye, Jiankun Wang
Localization Through Particle Filter Powered Neural Network Estimated Monocular Camera Poses
Yi Shen, Hao Liu, Xinxin Liu, Wenjing Zhou, Chang Zhou, Yizhou Chen
Localization of Pallets on Shelves Using Horizontal Plane Projection of a 360-degree Image
Yasuyo Kita, Yudai Fujieda, Ichiro Matsuda, Nobuyuki Kita