基于编解码双路卷积神经网络的视觉自定位方法

doi:10.13700/j.bh.1001-5965.2019.0046

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摘要为了从单张RGB图像估计出相机的位姿信息，提出了一种深度编解码双路卷积神经网络（CNN），提升了视觉自定位的精度。首先，使用编码器从输入图像中提取高维特征；然后，使用解码器提升特征的空间分辨率；最后，通过多尺度位姿预测器输出位姿参数。由于位置和姿态的特性不同，网络从解码器开始采用双路结构，对位置和姿态分别进行处理，并且在编解码之间增加跳跃连接以保持空间信息。实验结果表明：所提网络的精度与目前同类型算法相比有明显提升，其中相机姿态角度精度有较大提升。

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	贾瑞明
	刘圣杰
	李锦涛
	王赟豪
	潘海侠

关键词 ：视觉自定位, 编解码结构, 卷积神经网络(CNN), 跳跃连接, 双路网络

Abstract：In order to calculate the camera pose from a single RGB image, a deep encoder-decoder dual-stream convolutional neural network (CNN) is proposed, which can improve the accuracy of visual localization. The network first uses an encoder to extract advanced features from input images. Second, the spacialresolution is enhancedby a pose decoder.Finally, a multi-scale estimator is used to output pose parameters. Becauseof the differentperformance of position and orientation, the network adopts a dual-stream structure from the decoder to process the position and orientationseparately. To restore the spatial information, several skip connections are added to encoder-decoder architecture. The experimental results show that the accuracy of the network is obviously improved compared with the congener state-of-the-art algorithms, and the orientation accuracy of camera pose is improved dramatically.

Key words： visual localization encoder-decoder architecture convolutional neural network (CNN) skip connection dual-stream network

收稿日期: 2019-02-13

PACS:	V249.32+9
	TP391

基金资助:国家重点研发计划（2017YFB0802300）；北京市教委面上项目（KM201510009005）；北方工业大学学生科技活动项目（110051360007）

通讯作者: 贾瑞明 E-mail: jiaruiming@ncut.edu.cn

作者简介: 贾瑞明男,博士,助理研究员。主要研究方向:计算机视觉、深度学习、模式识别;刘圣杰男,硕士研究生。主要研究方向:计算机视觉、深度学习。

引用本文:

贾瑞明, 刘圣杰, 李锦涛, 王赟豪, 潘海侠. 基于编解码双路卷积神经网络的视觉自定位方法[J]. 北京航空航天大学学报, 2019, 45(10): 1965-1972.
JIA Ruiming, LIU Shengjie, LI Jintao, WANG Yunhao, PAN Haixia. A visual localization method based on encoder-decoder dual-stream CNN. JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2019, 45(10): 1965-1972.

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https://bhxb.buaa.edu.cn/CN/10.13700/j.bh.1001-5965.2019.0046 或 https://bhxb.buaa.edu.cn/CN/Y2019/V45/I10/1965

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