论文标题
室内移动机器人的本地化和导航系统
Localization and Navigation System for Indoor Mobile Robot
论文作者
论文摘要
视力障碍的人通常发现由于避免障碍和所需地点的指导问题，在许多公共场所（例如机场和购物中心）独立旅行。因此，在高度动态的室内环境中，如何改善室内导航机器人的定位和导航精度，以便它们指导视力障碍的良好良好成为问题。一种方法是使用视觉猛击。但是，典型的视觉大满贯要么假设一个静态环境，这可能会导致动态环境中的准确结果较少，或者假设目标都是动态的，并且可以删除上面的所有特征点，从而在很大程度上以可用的计算能力牺牲了计算速度。本文旨在探索室内导航机器人技术的边际定位和导航系统。所提出的系统旨在通过识别和跟踪潜在移动的对象并使用向量场直方图进行本地路径计划和避免障碍物来提高高度动态环境中的本地化和导航精度。该系统已在公共室内RGB-D数据集上进行了测试，结果表明，新系统可以提高准确性和鲁棒性，同时减少高度动态的室内场景中的计算时间。
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.
