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A new approach for mobile robot localization based on an online IoT system

机译：基于在线物联网系统的移动机器人本地化新方法

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In Mobile Robotics, localization is a primordial task, as it makes possible the navigation of the robot, thus enabling it to carry out its activities. From the emergence of the Internet of Things (IoT), there was a different approach of interacting objects with each other, as well as between objects and humans. Based on the context presented, this article evidences the utilization of IoT for the development of a system aimed for localization mobile robots employing Convolutional Neural Networks (CNN) in the process of feature extraction of the images, according to the concept of Transfer Learning. The mechanism uses the topological mapping method to orient themselves in the exploration environment considered. The effectiveness of the approach is demonstrated by parameters such as Accuracy, F1-score and time of processing. The IoT system confers the centralization of processing, reducing costs and allowing reuse of the robot's idle computing power. Combined with this benefit, CNN still achieves 100% Accuracy and F1-Score, proving to be an effective technique for the required activity. With this, the proposed approach demonstrates to be efficient for the use in the task of locating mobile robots. (C) 2019 Elsevier B.V. All rights reserved.
机译：在Mobile Robotics中，本地化是一项首要任务，因为它可以导航机器人，从而使其能够执行其活动。从物联网（IoT）的出现开始，就有了一种不同的方法来使对象彼此之间以及对象与人之间进行交互。基于传递的概念，本文证明了物联网在开发系统中的应用，该系统旨在针对使用卷积神经网络（CNN）进行定位的移动机器人进行图像特征提取。该机制使用拓扑映射方法将自己定位在所考虑的勘探环境中。该方法的有效性由诸如准确性，F1得分和处理时间之类的参数证明。物联网系统可实现集中处理，降低成本并允许重复使用机器人的空闲计算能力。结合此优势，CNN仍可达到100％的准确度和F1-Score，事实证明它是一项有效的所需活动技术。由此，所提出的方法证明在定位移动机器人的任务中是有效的。 （C）2019 Elsevier B.V.保留所有权利。

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