Precise Localization
Precise localization, the accurate determination of an object's position and orientation, is a crucial area of research with applications spanning augmented reality, manufacturing quality control, medical imaging, and robotics. Current research focuses on developing robust and efficient localization methods, often employing deep learning models like convolutional neural networks and transformers, alongside techniques such as occupancy networks and graph-based approaches, to overcome challenges posed by noisy data, occlusions, and dynamic environments. These advancements are significantly impacting various fields, enabling improvements in autonomous navigation, medical diagnostics, and the creation of high-fidelity 3D models for diverse applications. The development of more precise and computationally efficient localization techniques continues to be a major driver of innovation across numerous scientific disciplines.
Papers
Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs
Julia Werner, Christoph Gerum, Moritz Reiber, Jörg Nick, Oliver Bringmann
LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization
Mingrui Liu, Xinyang Tang, Yeqiang Qian, Jiming Chen, Liang Li