基于递归卷积神经网络的移动机器人定位算法

Recurrent Convolutional Neural Networks-Based Mobile Robot Localization Algorithm

移动机器人定位已成为机器人研究的重要任务。提出基于递归卷积神经网络的移动机器人定位(Recurrent Convolutional Neural Networks-Based Mobile Robot Localization,RCNN-MRL)算法。递归卷积神经网络(Recurrent Convolutional Neural Networks,RCNN)结合卷积神经网络(Convolutional Neural Networks,CNN)和递归神经网络(Recurrent Neural Networks,RNN)的特性,并依据机器人上嵌入的照相机拍摄的第一人称视角图像,RCNN-MRL算法利用RCNN实现自主定位。具体而言,先通过RCNN有效地处理多个连续图像,再利用RCNN作为回归模型,进而估计机器人位置。同时,设计双轮机器人移动,获取多个时间序列图像信息。最后,依据双轮机器人随机移动建立仿真环境,分析机器人定位性能。实验数据表明,提出的RCNN模型能够实现自主定位。

Mobile robot localization has been considered to be an important task in the ?eld of robotics research. This paper proposes Recurrent Convolutional Neural Networks-Based Mobile Robot Localization(RCNN-MRL)algorithm.RCNN(Recurrent Convolutional Neural Networks)is a neural networks model that combines Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN), and RCNN-MRL estimates the self-position from the first person view captured by a camera on a robot by using RCNN. Specifically, it uses a regression model for localization by using RCNN capable of processing consecutive images. This paper uses simulated environments where a two-wheel robot moves randomly, and analyzes the performance of localization. The experiments show that RCNN model can estimate the self-position of the robot.