Visually impaired people usually find it hard to travel independently in many public places such as airports and shopping malls due to the problems of obstacle avoidance and guidance to the desired location. Therefore, in the highly dynamic indoor environment, how to improve indoor navigation robot localization and navigation accuracy so that they guide the visually impaired well becomes a problem. One way is to use visual SLAM. However, typical visual SLAM either assumes a static environment, which may lead to less accurate results in dynamic environments or assumes that the targets are all dynamic and removes all the feature points above, sacrificing computational speed to a large extent with the available computational power. This paper seeks to explore marginal localization and navigation systems for indoor navigation robotics. The proposed system is designed to improve localization and navigation accuracy in highly dynamic environments by identifying and tracking potentially moving objects and using vector field histograms for local path planning and obstacle avoidance. The system has been tested on a public indoor RGB-D dataset, and the results show that the new system improves accuracy and robustness while reducing computation time in highly dynamic indoor scenes.
翻译：视觉受损者通常发现在许多公共场所难以独立旅行,如机场和购物中心等,因为有障碍避免问题和对理想地点的指导问题,因此,在高度动态的室内环境中,如何改进室内导航机器人定位和导航准确性以引导视障井形成问题,一种办法是使用视觉SLAM。然而,典型的视觉 SLAM要么假设一个静态环境,这可能导致动态环境中的结果不那么准确,要么假设目标全部是动态的,并排除所有上述特征点,大大牺牲计算速度,利用现有计算能力。本文试图探索室内导航机器人的边际本地化和导航系统。拟议的系统旨在通过识别和跟踪可能移动的物体并利用矢量场直方图进行本地路径规划和避免障碍,提高高度动态环境中的本地化和导航准确性。该系统已在一个公共室内RGB-D数据集上进行了测试,结果显示,新系统在减少高度动态室内场的计算时间的同时,提高了准确性和稳健性。
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