Deep trail-following robotic guide dog in pedestrian environments for people who are blind and visually impaired - Learning from virtual and real worlds

Tzu Kuan Chuang, Ni Ching Lin, Jih Shi Chen, Chen Hao Hung, Yi Wei Huang, Chunchih Tengl, Haikun Huang, Lap Fai Yu, Laura Giarre, Hsueh-Cheng Wang

研究成果: Paper同行評審

45 引文 斯高帕斯(Scopus)

摘要

Navigation in pedestrian environments is critical to enabling independent mobility for the blind and visually impaired (BVI) in their daily lives. White canes have been commonly used to obtain contact feedback for following walls, curbs, or man-made trails, whereas guide dogs can assist in avoiding physical contact with obstacles or other pedestrians. However, the infrastructures of tactile trails or guide dogs are expensive to maintain. Inspired by the autonomous lane following of self-driving cars, we wished to combine the capabilities of existing navigation solutions for BVI users. We proposed an autonomous, trail-following robotic guide dog that would be robust to variances of background textures, illuminations, and interclass trail variations. A deep convolutional neural network (CNN) is trained from both the virtual and realworld environments. Our work included major contributions: 1) conducting experiments to verify that the performance of our models trained in virtual worlds was comparable to that of models trained in the real world; 2) conducting user studies with 10 blind users to verify that the proposed robotic guide dog could effectively assist them in reliably following man-made trails.

原文American English
頁面5849-5855
頁數7
DOIs
出版狀態Published - 10 9月 2018
事件2018 IEEE International Conference on Robotics and Automation, ICRA 2018 - Brisbane, Australia
持續時間: 21 5月 201825 5月 2018

Conference

Conference2018 IEEE International Conference on Robotics and Automation, ICRA 2018
國家/地區Australia
城市Brisbane
期間21/05/1825/05/18

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