Table of Contents Author Guidelines Submit a Manuscript
Journal of Sensors
Volume 2016 (2016), Article ID 3754918, 13 pages
Research Article

Real-Time Obstacle Detection System in Indoor Environment for the Visually Impaired Using Microsoft Kinect Sensor

1Université Grenoble-Alpes, AGIM/AGEIS, 38706 La Tronche, France
2International Research Institute MICA, HUST-CNRS/UMI-2954-GRENOBLE INP and HUST, 1 Dai Co Viet, Hanoi, Vietnam
3Institut Universitaire de France, 75231 Paris, France
4LAI Jean-Raoul Scherrer, University of Geneva, Switzerland and University of Grenoble Alpes, 38041 Saint-Martin-d’Hères, France

Received 27 July 2015; Revised 22 October 2015; Accepted 25 October 2015

Academic Editor: Hai-Feng Ji

Copyright © 2016 Huy-Hieu Pham et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Any mobility aid for the visually impaired people should be able to accurately detect and warn about nearly obstacles. In this paper, we present a method for support system to detect obstacle in indoor environment based on Kinect sensor and 3D-image processing. Color-Depth data of the scene in front of the user is collected using the Kinect with the support of the standard framework for 3D sensing OpenNI and processed by PCL library to extract accurate 3D information of the obstacles. The experiments have been performed with the dataset in multiple indoor scenarios and in different lighting conditions. Results showed that our system is able to accurately detect the four types of obstacle: walls, doors, stairs, and a residual class that covers loose obstacles on the floor. Precisely, walls and loose obstacles on the floor are detected in practically all cases, whereas doors are detected in 90.69% out of 43 positive image samples. For the step detection, we have correctly detected the upstairs in 97.33% out of 75 positive images while the correct rate of downstairs detection is lower with 89.47% from 38 positive images. Our method further allows the computation of the distance between the user and the obstacles.