This paper presents a new holistic vision-based mobile assistive navigation system to help blind and visually impaired people with indoor independent travel. The system detects dynamic obstacles and adjusts path planning in real-time to improve navigation safety. First, we develop an indoor map editor to parse geometric information from architectural models and generate a semantic map consisting of a global 2D traversable grid map layer and context-aware layers. By leveraging the visual positioning service (VPS) within the Google Tango device, we design a map alignment algorithm to bridge the visual area description file (ADF) and semantic map to achieve semantic localization. Using the on-board RGB-D camera, we develop an efficient obstacle detection and avoidance approach based on a time-stamped map Kalman filter (TSM-KF) algorithm. A multi-modal human-machine interface (HMI) is designed with speech-audio interaction and robust haptic interaction through an electronic SmartCane. Finally, field experiments by blindfolded and blind subjects demonstrate that the proposed system provides an effective tool to help blind individuals with indoor navigation and wayfinding.

Independent travel is a well known challenge for blind or visually impaired persons. In this paper, we propose a computer vision-based indoor wayfinding system for assisting blind people to independently access unfamiliar buildings. In order to find different rooms (i.e. an office, a lab, or a bathroom) and other building amenities (i.e. an exit or an elevator), we incorporate door detection with text recognition. First we develop a robust and efficient algorithm to detect doors and elevators based on general geometric shape, by combining edges and corners. The algorithm is generic enough to handle large intra-class variations of the object model among different indoor environments, as well as small inter-class differences between different objects such as doors and elevators. Next, to distinguish an office door from a bathroom door, we extract and recognize the text information associated with the detected objects. We first extract text regions from indoor signs with multiple colors. Then text character localization and layout analysis of text strings are applied to filter out background interference. The extracted text is recognized by using off-the-shelf optical character recognition (OCR) software products. The object type, orientation, and location can be displayed as speech for blind travelers.