Learning Representative Features for Robot Topological Localization Regular Paper

doi:10.5772/56324

	Learning Representative Features for Robot Topological Localization Regular Paper
	Zhao, Zeng-Shun 1,2; Feng, Xiang 2; Wei, Fang 2; Lin, Yan-Yan 2; Li, Yi-Bin 1; Hou, Zeng-Guang3 ; Tan, Min3
发表期刊	INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
	2013-04-26
卷号	10
文章类型	Article
摘要	This paper proposes a new method for mobile robots to recognize places with the use of a single camera and natural landmarks. In the learning stage, the robot is manually guided along a path. Video sequences are captured with a front-facing camera. To reduce the perceptual alias of visual features, which are easily confused, we propose a modified visual feature descriptor which combines the dominant hue colour information with the local texture. A Location Features Vocabulary Model (LVFM) is established for each individual location using an unsupervised learning algorithm. During the course of travelling, the robot employs each detected interest point to vote for the most likely place. The spatial relationships between the locations, modelled by the Hidden Markov Model (HMM), are exploited to increase the robustness of location recognition in cases of dynamic change or visual similarity. The proposed descriptors are compared with several state-of-the-art descriptors including SIFT, colour SIFT, GLOH and SURF. Experiments show that both the LVFM based on the dominant Hue-SIFT feature and the spatial relationships between the locations contribute considerably to the high recognition rate.
关键词	Vision-based Localization Hidden Markov Model Invariant Feature Competitive Learning
WOS标题词	Science & Technology ; Technology
DOI	10.5772/56324
关键词[WOS]	MOBILE ROBOTS ; DESCRIPTORS ; NAVIGATION
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(60805028 ; Natural Science Foundation of Shandong Province(ZR2010FM027) ; China Postdoctoral Science Foundation(2012M521336) ; Open Research Project from SKLMCCS(20120105) ; SDUST Research Fund(2010KYTD101) ; 61175076 ; 61225017)
WOS研究方向	Robotics
WOS类目	Robotics
WOS记录号	WOS:000318219300002
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3493
专题	复杂系统认知与决策实验室_先进机器人
作者单位	1.Shandong Univ, Sch Control Sci & Engn, Jinan 250100, Peoples R China 2.Shandong Univ Sci & Technol, Coll Informat & Elect Engn, Qingdao, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Zeng-Shun,Feng, Xiang,Wei, Fang,et al. Learning Representative Features for Robot Topological Localization Regular Paper[J]. INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,2013,10.
APA	Zhao, Zeng-Shun.,Feng, Xiang.,Wei, Fang.,Lin, Yan-Yan.,Li, Yi-Bin.,...&Tan, Min.(2013).Learning Representative Features for Robot Topological Localization Regular Paper.INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,10.
MLA	Zhao, Zeng-Shun,et al."Learning Representative Features for Robot Topological Localization Regular Paper".INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 10(2013).