Applying Deep Learning and Building Information Modeling to Indoor Positioning Based on Sound

At present, indoor positioning has great potential for disaster mitigation, such as guiding evacuees to safe places. This research aims at developing such a sound-based method using artificial intelligence (AI) and building information modeling (BIM). Amid a disaster, first responders can quickly set up the proposed system to help indoor positioning, which relies on BIM, virtual reality (VR), and head related transfer functions (HRTF) techniques to simulate virtual sound fields. Then, a deep learning model is trained so as to be able to predict the current zone within a room based on the sound received. Unity, a serious game platform, and Steam Audio, a Unity plugin designed for adding 3D audio to VR experience, are employed to generate input data sets. The overall accuracy of the output results is about 90% though the training time is long, which can be reduced if more powerful computing resources are utilized.