In this study, a semantic segmentation network is presented to develop an indoor navigation system for a mobile robot. Semantic segmentation can be applied by adopting different techniques, such as a convolutional neural network (CNN). However, in the present work, a residual neural network is implemented by engaging in ResNet-18 transfer learning to distinguish between the floor, which is the navigation free space, and the walls, which are the obstacles. After the learning process, the semantic segmentation floor mask is used to implement indoor navigation and motion calculations for the autonomous mobile robot. This motion calculations are based on how much the estimated path differs from the center vertical line. The highest point is used to move the motors toward that direction. In this way, the robot can move in a real scenario by avoiding different obstacles. Finally, the results are collected by analyzing the motor duty cycle and the neural network execution time to review the robot’s performance. Moreover, a different net comparison is made to determine other architectures’ reaction times and accuracy values.

中文翻译：

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语义分割为自主移动机器人开发室内导航系统

在这项研究中，提出了语义分割网络以开发用于移动机器人的室内导航系统。可以通过采用不同技术（例如卷积神经网络（CNN））来应用语义分割。但是，在目前的工作中，通过参与ResNet-18转移学习来实现残差神经网络，以区分地板（导航自由空间）和墙壁（障碍）。在学习过程之后，语义分割地板面具被用于实现自主移动机器人的室内导航和运动计算。此运动计算基于估计路径与中心垂直线的差异。最高点用于使电动机朝该方向移动。通过这种方式，机器人可以通过避免不同的障碍物在真实场景中运动。最后，通过分析电机占空比和神经网络执行时间来收集结果，以查看机器人的性能。此外，进行了不同的净比较，以确定其他体系结构的反应时间和准确性值。