2 Dimensional Map

Two-dimensional maps are increasingly central to robotics and computer vision research, focusing on efficient and robust methods for navigation, localization, and environment understanding. Current research emphasizes leveraging learned representations, such as masked autoencoders and transformer networks, to process and interpret these maps, often integrating them with semantic information and object recognition for improved accuracy. This work is driven by the need for efficient and scalable solutions for autonomous systems, augmented reality applications, and human-robot interaction, moving beyond reliance on computationally expensive 3D models. The development of novel map representations and algorithms promises significant advancements in these fields.