Where am I in the dark: Exploring active transfer learning on the use of indoor localization based on thermal imaging

Indoor localization is one of the key problems in robotics research. Most current localization systems use cellular base stations and Wifi signals, whose localization accuracy is largely dependent on the signal strength and is sensitive to environmental changes. With the development of camera-based technologies, image-based localization may be employed in an indoor environment where the GPS signal is weak. Most of the existing image-based localization systems are based on color images captured by cameras, but this is only feasible in environments with adequate lighting conditions. In this paper, we introduce an image-based localization system based on thermal imaging to make the system independent of light sources, which are especially useful during emergencies such as a sudden power outage in a building. As thermal images are not obtained as easily as color images, we apply active transfer learning to enrich the thermal image classification learning, where normal RGB images are treated as the source domain, and thermal images are the target domain. The application of active transfer learning avoids random target training sample selection and chooses the most informative samples in the learning process. Through the proposed active transfer learning, the query thermal images can be accurately used to indicate the location. Experiments show that our system can be efficiently deployed to perform indoor localization in a dark environment.