| Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features | |
|---|---|
| 學年 | 101 |
| 學期 | 1 |
| 出版（發表）日期 | 2013-01-01 |
| 作品名稱 | Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features |
| 作品名稱（其他語言） | |
| 著者 | Wang, Yin-tien; Chi, Chen-tung; Feng, Ying-chieh |
| 單位 | 淡江大學機械與機電工程學系 |
| 出版者 | Stafa-Zurich: Trans Tech Publications Ltd. |
| 著錄名稱、卷期、頁數 | Applied Mechanics and Materials 284-287, pp.2142-2146 |
| 摘要 | An algorithm for robot mapping is proposed in this paper using the method of speeded-up robust features (SURF). Since SURFs are scale- and orientation-invariant features, they have higher repeatability than that of the features obtained by other detection methods. Even in the cases of using moving camera, the SURF method can robustly extract the features from image sequences. Therefore, SURFs are suitable to be utilized as the map features in visual simultaneous localization and mapping (SLAM). In this article, the procedures of detection and matching of the SURF method are modified to improve the image processing speed and feature recognition rate. The sparse representation of SURF is also utilized to describe the environmental map in SLAM tasks. The purpose is to reduce the computation complexity in state estimation using extended Kalman filter (EKF). The EKF SLAM with SURF-based map is developed and implemented on a binocular vision system. The integrated system has been successfully validated to fulfill the basic capabilities of SLAM system. |
| 關鍵字 | Robot Mapping;Speeded-Up Robust Features (SURF);EKF-SLAM |
| 語言 | en |
| ISSN | 1662-7482 |
| 期刊性質 | 國外 |
| 收錄於 | EI |
| 產學合作 | |
| 通訊作者 | Wang, Y.T. |
| 審稿制度 | 是 |
| 國別 | CHE |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/92585 ) |
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