论文标题
卷积神经网络朝向室内环境的Arduino导航
Convolutional Neural Networks Towards Arduino Navigation of Indoor Environments
论文作者
论文摘要
在本文中，我们提出了许多经过测试的方式，可以使低预算的演示汽车在室内环境中导航。 Canny边缘检测，监督的地板检测和模仿学习分别使用，并在其有效性上形成鲜明对比。本文中使用的设备近似于配置用于与移动设备一起用于图像处理的自主机器人。本文没有提供确定的解决方案，并简单地说明了成功实现室内环境自动导航所采取的方法。记录并阐述了所有方法的成功和失败，以使读者深入了解这种自主机器人的构建。
In this paper we propose a number of tested ways in which a low-budget demo car could be made to navigate an indoor environment. Canny Edge Detection, Supervised Floor Detection and Imitation Learning were used separately and are contrasted in their effectiveness. The equipment used in this paper approximated an autonomous robot configured to work with a mobile device for image processing. This paper does not provide definitive solutions and simply illustrates the approaches taken to successfully achieve autonomous navigation of indoor environments. The successes and failures of all approaches were recorded and elaborated on to give the reader an insight into the construction of such an autonomous robot.
