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研究生:楊忠諺
研究生(外文):Zhong-yan Yang
論文名稱:以單一FPGA平台實現SIFT法則於自走車的地標物之視覺辨識與搜索
論文名稱(外文):Single Platform of FPGA-Based SIFT for Visual Recognition and Landmark Searching of a Mobile Robot
指導教授:黃志良黃志良引用關係
指導教授(外文):Chih-lyang Hwang
口試委員:黃志良
口試委員(外文):Chih-lyang Hwang
口試日期:2014-06-23
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:59
中文關鍵詞:FPGA平台SIFT演算法影像辨識線性搜索的匹配準則自走車地標物的搜索策略
外文關鍵詞:FPGA platformSIFT algorithmVisual recognitionMatching criterionMobile robotSearching strategy of specific landmark.
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眾所周知,特徵的萃取為影像處理的物件辨識之基本重要步驟,為了達到萃取此較強健之特徵點,軟體執行相關演算法所需之計算較難以達到即時的效果,有鑑於此,本論文將以FPGA平台實現SIFT演算法達到快速而且能夠獲得較強健之特徵點的計算及描述。本論文亦選定滅火器及垃圾桶為地標物,並事先將此特徵描述向量儲存於FPGA平台中,並設計地標物的搜索策略,接著以自走車上的FPGA平台進行相關地標物之搜索。並以線性搜索的匹配準則,自走車在可辨識之距離進行地標物的搜索、辯識及匹配比對,最後以相關的實驗驗證所發展的平台執行SIFT演算法和地標物搜索策略之有效性及強健性。
It is known that feature extraction is a fundamental part to recognize objects in an image processed. For the purpose of robustly extracting these features, the demanded computation using software approach is difficult to obtain on-line applications. In this thesis, an FPGA-based scale invariant feature transform (SIFT) is implemented to accelerate the recognition and description of these features. The extinguisher and garbage can are chosen as specific landmarks and they are also pre-trained to accomplish their corresponding feature vectors stored in the FPGA platform. The matching criterion using linear search is then constructed to evaluate the successful recognition rate of the specific landmarks. The strategy for the search of the chosen landmarks through a mobile robot is designed and evaluated by different conditions. Finally, the corresponding experiments are given to validate the effectiveness and robustness of the proposed methodology.
第一章 序論
1.1研究動機
1.2論文架構
第二章 系統架構
2.1系統介紹
2.2系統整合
第三章 硬體電路及訊號流程說明
3.1 FPGA內部硬體電路
3.2視覺搜索電路
第四章 SIFT演算法
4.1尺度空間極值檢測
4.2極值點篩選
4.3決定特徵點方向
4.4建構特徵點描述向量
4.5 SIFT特徵點辨識
第五章 實現SIFT硬體電路
5.1多層圖像平行處理概念
5.2 FPGA執行迴旋積運算
第六章 實驗結果與討論
6.1 FPGA平台執行SIFT效能與PC平台做比較
6.2測試FPGA平台執行SIFT辨識能力
6.3 FPGA平台執行SIFT所消耗資源
6.4實驗介紹
6.5實驗結果
6.6討論
第七章 結論與未來展望
參考文獻
附錄
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[5] X. Yang, M. Moallem and R.V. Patel, “A layer global-oriented fuzzy motion planning strategy for mobile robot navigation,” IEEE Syst. Man &; Cybern., Part B, vol. 35, no. 6, pp. 1214-1224, Dec. 2005.
[6] A. M. Ladd, K.E. Bekris, A. P. Rudys, D.S. Wallach and L. E. Kavraki, “ On the feasibility of using wireless Ethernet for indoor localization,” IEEE Trans. Robotics &; Autom., vol. 20, no. 3, pp. 555-559, Jun. 2004.
[7] J. G. Davis, R. Sloan, and A. J. Peyton, “The 3-D positioning of wireless sensors in dispersive propagation media,” IEEE Trans. Instrum. Meas., vol. 62, no. 8, pp. 2338-2352, Aug. 2013.
[8] D. Schleicher, L. M. Bergasa, M. Ocana, R. Barea and M. E. Lopez, “Real-time hierarchical outdoor SLAM based on stereovision and GPS fusion,” IEEE Trans. Intell. Trans. Syst., vol. 10, no. 3, pp. 440-452, Sep. 2009.
[9] E. Menegatti, A. Pretto, A. Scarpa, and E. Pagello, “Omni-directional vision scan matching for robot localization in dynamic environments,” IEEE Trans. Robotics, vol. 22, no. 3, pp. 523-535, Jun. 2006.
[10]H. C. Huang and C. C. Tsai, “FPGA implementation of an embedded robust adaptive controller for autonomous omnidirectional mobile platform,” IEEE Trans. Ind. Electron., vol. 56, no. 5, pp.1604-1616, May 2009.
[11] S. K. Su, “A Comparison of Vision-Based Autonomous Navigation for Target Grasping of Humanoid Robot by Enhanced SIFT and Traditional HT Algorithms”pp.16-21, May 2013
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