Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. Previous works focus on obstacle avoidance, however they do not have a general view of the environment they are moving in. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people. Based on traditional systems that use RGB-D cameras for obstacle avoidance, it is included and combined the information of a fish-eye camera, which will give a better understanding of the user's surroundings. The combination gives robustness and reliability to the system as well as a wide field of view that allows to obtain many information from the environment. This combination of sensors is inspired by human vision where the center of the retina (fovea) provides more accurate information than the periphery, where humans have a wider field of view. The proposed system is mounted on a wearable device that provides the obstacle-free zones of the scene, allowing the planning of trajectories for people guidance.

翻译：在未知环境中寻找无障碍路径是视障人员与自主机器人面临的一大导航难题。以往的研究侧重于避障，但缺乏对移动环境中整体场景的宏观认知。基于计算机视觉系统的新型设备能够帮助视障人员在安全条件下克服未知环境导航的困难。本文提出了一种融合传感器与算法的方案，旨在构建面向视障人员的导航系统。该系统以采用RGB-D相机实现避障的传统系统为基础，引入并整合了鱼眼相机的信息，从而增强对用户周边环境的理解。这种传感器融合不仅提升了系统的鲁棒性与可靠性，还通过宽视场角获取丰富的环境信息。该融合方案受人类视觉机制启发：视网膜中心（中央凹）提供比外周更精确的信息，而外周则赋予人类更广阔的视野。所提出的系统安装于可穿戴设备上，能够实时解算场景中的无障碍区域，进而实现人员引导的轨迹规划。