COSINE (Context-aware Opportunistic Sensing for Indoor Navigation Environment)

In the absence of a global reference such as GPS, many efforts focus on using IMU sensors or a hybrid approach of using IMU sensors with camera, LiDAR, and Wi-Fi or Bluetooth beacons for accurate indoor positioning and navigation. As these auxiliary sensors bring inconvenience and leads to increased cost, pedestrian dead reckoning (PDR) based on IMU alone has received much attention. Nevertheless, existing PDR based on Kalman Filtering and machine learning techniques fail to predict under varying device placements and human motion especially when riding on an elevator or climbing stairs where steps cannot be accurately predicted. As a solution, we propose to research and develop an innovative, context-aware pedestrian dead reckoning technology called COSINE (Context-aware Opportunistic Sensing for Indoor Navigation Environment) that can predict user positions and trajectories with less than 0.2% error. COSINE relies on raw sensor data streams and advanced analytics to sense the context passively and implicitly around the user, and then predict position and orientations under a given context. The core of our approach stems from the exploitation of edge-friendly temporal deep learning architectures with adversarial learning inspired feature denoising and context-aware sensing to predict user trajectories under unpredictable sensor noises and contexts.