Floor Identification

Floor identification research focuses on accurately determining the floor level within a building using various sensor data and machine learning techniques. Current approaches leverage diverse data sources, including RF signals, inertial measurement units, and visual imagery from drones, employing algorithms such as graph neural networks, convolutional neural networks, and random forests to achieve robust floor identification. These advancements are crucial for improving indoor localization in smart cities, optimizing warehouse logistics, and enhancing construction site safety through automated fall hazard detection. The development of accurate and efficient floor identification systems has significant implications for robotics, building management, and various other applications requiring precise indoor spatial awareness.