Robot-assisted smartphone localization for human indoor tracking

Smartphone-based human indoor localization was previously implemented using wireless sensor networks at the cost of sensing infrastructure deployment. Motivated by increasing research attention on location-aware human–robot interaction, we propose a robot-assisted human indoor localization scheme utilizing acoustic ranging between a self-localized mobile robot and smartphones. Data from the low-cost Kinect vision sensor are fused with smartphone-based acoustic ranging, and an extended Kalman filter based localization algorithm is developed for real-time dynamic position estimation and tracking. Real robot–smartphone experiments are performed, and performances are evaluated in various indoor environments under different environmental noises and with different human walking speed. Comparing to existing indoor smartphone localization methods, the proposed system does not rely on wireless sensing infrastructure, and has comparable localization accuracy with increased flexibility and scalability due to the mobility of the robot.