Unsupervised Feature Learning for Visual Place Recognition in Changing Environments

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 机器人学研究室

	Unsupervised Feature Learning for Visual Place Recognition in Changing Environments
	Zhao DY(赵冬晔)1,3,4; Si BL(斯白露)2; Tang FZ(唐凤珍)1,4
	2019
会议日期	July 14-19, 2019
会议地点	Budapest, Hungary
关键词	visual place recognition changing environments unsupervised learning siamese VisNet
页码	1-8
英文摘要	Visual place recognition in changing environments is a challenging and critical task for autonomous robot navigation. Deep convolutional neural networks (ConvNets) have recently been used as efficient feature extractors and obtained excellent performance in place recognition. However the success of Con-vNets' learning highly relies on the availability of large datasets with millions of labeled images, the collection of which is a tedious and costly burden. Thus we develop an unsupervised learning method (the siamese VisNet) to autonomously learn invariant features in changing environments from unlabeled images. The siamese VisNet has two identical branches of sub-networks. With a Hebbian-type of learning rule incorporating a trace of previous activity patterns, the siamese VisNet learns features with increasing invariance in changing environments from layer to layer. Experiments conducted on multiple datasets demonstrate the robustness of the siamese VisNet against viewpoint changes, appearance changes, and joint viewpoint-appearance changes. In addition, the siamese VisNet, with lower complexity in architecture, outperforms the state-of-the-art place recognition ConvNets such as the CaffeNet and the PlaceNet. The proposed siamese VisNet constitutes a biologically plausible yet efficient method for unsupervised place recognition.
产权排序	1
会议录	Proceedings of the International Joint Conference on Neural Networks
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-7281-1985-4
WOS记录号	WOS:000530893806013
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/25780]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Zhao DY(赵冬晔)
作者单位	1.State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, China 2.School of Systems Science, Beijing Normal University, Beijing, China 3.University of Chinese Academy of Sciences, Beijing, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
推荐引用方式 GB/T 7714	Zhao DY,Si BL,Tang FZ. Unsupervised Feature Learning for Visual Place Recognition in Changing Environments[C]. 见:. Budapest, Hungary. July 14-19, 2019.