A SLAM Based Semantic Indoor Navigation System for Visually Impaired Users
This paper proposes a novel assistive navigation system based on simultaneous localization and mapping (SLAM) and semantic path planning to help visually impaired users navigate in indoor environments. The system integrates multiple wearable sensors and feedback devices including a RGB-D sensor and an inertial measurement unit (IMU) on the waist, a head mounted camera, a microphone and an earplug/speaker. We develop a visual odometry algorithm based on RGB-D data to estimate the user's position and orientation, and refine the orientation error using the IMU. We employ the head mounted camera to recognize the door numbers and the RGB-D sensor to detect major landmarks such as corridor corners. By matching the detected landmarks against the corresponding features on the digitalized floor map, the system localizes the user, and provides verbal instruction to guide the user to the desired destination. The software modules of our system are implemented in Robotics Operating System (ROS). The prototype of the proposed assistive navigation system is evaluated by blindfolded sight persons. The field tests confirm the feasibility of the proposed algorithms and the system prototype.