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

The COSINE STTR Phase II is established with the rigorous goal of realizing human pose estimation and localization with less than 0.2% error rate of the ground truth position in GPS denied and contested environments, without relying on any infrastructure. Unknot.id Inc., proposes to research, develop, and demonstrate an innovative, multi-modal, cost-effective, user and motion-dynamics invariant, artificial intelligence (AI) powered robust human positioning and identification platform called COSINE (Context-aware Opportunistic Sensing for Indoor Navigation Environment). The core of our technology lies on the exploitation of novel sensor modalities and the advancements in deep learning technology from training to learning algorithms on multi-modal noisy sensors readily accessible in a smartphone to accurately localize and identify humans in GPS-denied and contested environments like low RF signal power (tree canopy, buildings, storage containers) and low lighting environments (e.g., dark, smoke-filled room) irrespective of the underlying human motion dynamics and device orientations.  COSINE is a Software-As-A-Service, multi-modal pose estimation and localization platform that facilitates every user (aka device) to compute its own six degrees of freedom (6DOF) pose, identity and context in real-time by optimally fusing audio, camera, three-axis accelerometer, three-axis gyroscope, three-axis magnetometer, light, and barometer sensors, without the need of any bespoke positioning infrastructure.