In this paper, we propose a deep learning based assistive system to improve
the environment perception experience of visually impaired (VI). The system is
composed of a wearable terminal equipped with an RGBD camera and an earphone, a
powerful processor mainly for deep learning inferences and a smart phone for
touch-based interaction. A data-driven learning approach is proposed to predict
safe and reliable walkable instructions using RGBD data and the established
semantic map. This map is also used to help VI understand their 3D surrounding
objects and layout through well-designed touchscreen interactions. The
quantitative and qualitative experimental results show that our learning based
obstacle avoidance approach achieves excellent results in both indoor and
outdoor datasets with low-lying obstacles. Meanwhile, user studies have also
been carried out in various scenarios and showed the improvement of VI's
environment perception experience with our system.

本文提出了一种基于深度学习的辅助系统，旨在提高视障人士的环境感知体验，通过 RGBD 数据和建立的语义地图预测行走指令、进行障碍物避让，在多种情境下得到了很好的实验效果并有效提高了视障人士的环境感知体验。