Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (10): 240-243.DOI: 10.3778/j.issn.1002-8331.1801-0141

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Recurrent Convolutional Neural Networks-Based Mobile Robot Localization Algorithm

LI Shaowei1, WANG Shengzheng2   

  1. 1.Faculty of Computer Science and Technology, School of Mathematics and Computer Science, Jianghan University, Wuhan 430056, China
    2.Faculty of Navigation, Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
  • Online:2019-05-15 Published:2019-05-13

基于递归卷积神经网络的移动机器人定位算法

李少伟1,王胜正2   

  1. 1.江汉大学 数学与计算机科学学院 计算机科学与技术系,武汉 430056
    2.上海海事大学 商船学院 航海系,上海 201306

Abstract: 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.

Key words: mobile robot localization, first person view, time series image, convolutional neural networks, two-wheel robot

摘要: 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。

关键词: 移动机器人定位, 第一人称视角, 时间序列图像, 递归卷积神经网络, 双轮机器人