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.
翻译:在本文中,我们建议了一些经过测试的低预算示范车可以驾驶室内环境的方法。Canny idege探测、监督地板探测和模拟学习是分开使用,其效果与之不同。本文中所使用的设备大致上是一个自主机器人,配置成一个移动处理图像设备。本文没有提供明确的解决办法,而只是说明为成功实现室内环境自主导航所采取的方法。所有方法的成功和失败都记录下来,并详细阐述,使读者能够深入了解这种自主机器人的构造。