Learning Representative Features for Robot Topological Localization Regular Paper
文献类型：期刊论文
| 作者 | Zhao, Zeng-Shun1,2; Feng, Xiang2; Wei, Fang2; Lin, Yan-Yan2; Li, Yi-Bin1; Hou, Zeng-Guang3 ; Tan, Min3
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| 刊名 | INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
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| 出版日期 | 2013-04-26 |
| 卷号 | 10 |
| 关键词 | Vision-Based Localization Hidden Markov Model Invariant Feature Competitive Learning |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Robotics |
| 研究领域[WOS] | Robotics |
| 关键词[WOS] | MOBILE ROBOTS ; DESCRIPTORS ; NAVIGATION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000318219300002 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3493]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 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). |
入库方式： OAI收割
来源：自动化研究所
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