A Wearable Indoor Navigation System with Context-based Decision Making For Visually Impaired

Abstract: This paper presents a wearable indoor navigation system that helps visually impaired user to perform indoor navigation. The system takes advantage of the Simultaneous Localization and Mapping (SLAM) and semantic path planning to accomplish localization and navigation tasks while collaborating with the visually impaired user. It integrates multiple sensors and feedback devices as an RGB-D camera, an IMU and a web camera; and it applies the RGB-D based visual odometry algorithm to estimate the user’s location and orientation, and uses the IMU to refine the orientation error. Major landmarks such as room numbers and corridor corners are detected by the web camera and RGB-D camera, and matched to the digitalized floor map so as to localize the user. The path and motion guidance are generated afterwards to guide the user to a desired destination. To improve the fitting between the rigid commands and optimal machine decisions for human beings, we propose a context based decision making mechanism on path planning that resolves users’ confusions caused by incorrect observations. The software modules are implemented in Robotics Operating System (ROS) and the navigation system are tested with blindfolded sight persons. The field experiments confirm the feasibility of the system prototype and the proposed mechanism.