Indoor Localization

Indoor localization aims to accurately determine the position of a person or object within an indoor environment, overcoming the limitations of GPS. Current research emphasizes improving accuracy and robustness using diverse data sources (WiFi RSSI, camera images, LiDAR, inertial sensors, audio), often employing deep learning models like transformers, convolutional neural networks, and graph neural networks, along with techniques like federated learning and transfer learning to address data scarcity and device heterogeneity. This field is crucial for numerous applications, including smart homes, robotics, healthcare, and industrial automation, driving advancements in both machine learning algorithms and sensor technologies.