  This paper presents a wearable assistive device with the shape of a pair of
eyeglasses that allows visually impaired people to navigate safely and quickly
in unfamiliar environment, as well as perceive the complicated environment to
automatically make decisions on the direction to move. The device uses a
consumer Red, Green, Blue and Depth (RGB-D) camera and an Inertial Measurement
Unit (IMU) to detect obstacles. As the device leverages the ground height
continuity among adjacent image frames, it is able to segment the ground from
obstacles accurately and rapidly. Based on the detected ground, the optimal
walkable direction is computed and the user is then informed via converted beep
sound. Moreover, by utilizing deep learning techniques, the device can
semantically categorize the detected obstacles to improve the users' perception
of surroundings. It combines a Convolutional Neural Network (CNN) deployed on a
smartphone with a depth-image-based object detection to decide what the object
type is and where the object is located, and then notifies the user of such
information via speech. We evaluated the device's performance with different
experiments in which 20 visually impaired people were asked to wear the device
and move in an office, and found that they were able to avoid obstacle
collisions and find the way in complicated scenarios.
