Indoor Environment

Indoor environment research focuses on understanding and optimizing the physical and digital aspects of indoor spaces, aiming to improve human experience, safety, and efficiency. Current research emphasizes advancements in computer vision for object detection and scene understanding, utilizing architectures like convolutional neural networks (CNNs) and transformers, alongside novel approaches in robotic navigation and mapping leveraging techniques such as simultaneous localization and mapping (SLAM) and graph neural networks (GNNs). These efforts are significant for applications ranging from assistive technologies for the visually impaired to improved building design and efficient autonomous systems for inspection and delivery. Furthermore, research is exploring the use of large language models (LLMs) for interior design and the creation of digital twins for indoor environments.