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
AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards
Uddhav Bhattarai, Santosh Bhusal, Qin Zhang, Manoj Karkee
Go-SLAM: Grounded Object Segmentation and Localization with Gaussian Splatting SLAM
Phu Pham, Dipam Patel, Damon Conover, Aniket Bera
Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning
Bryce T. Bolin, Michael W. Coughlin
Evaluating ML Robustness in GNSS Interference Classification, Characterization \& Localization
Lucas Heublein, Tobias Feigl, Thorsten Nowak, Alexander Rügamer, Christopher Mutschler, Felix Ott