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研究生:林威丞
研究生(外文):Wei-Cheng Lin
論文名稱:以單應性矩陣為基礎之機器人定位之探討
論文名稱(外文):A Survey of Homography Matrix Based Localization for Autonomous Robots
指導教授:林建州林建州引用關係
指導教授(外文):Chien-chou Lin
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊工程系碩士班
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:2013
畢業學年度:101
語文別:中文
論文頁數:53
中文關鍵詞:加速穩健的特徵機器人定位尺度不變特徵轉換單應性矩陣
外文關鍵詞:autonomous robotSURFHomographySIFTrobot localization
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本論文目標是分析目前以單應性矩陣做為機器人定位的方法中,常用的兩種圖像匹配法 (SIFT 和 SURF) 對定位結果之影響。以單應性矩陣做為機器人定位的方法大都預先收集場景中特徵圖片,如指示牌、海報等,並量測其在場景的相關資訊,並建立資料圖庫。藉由機器人移動時所拍攝之當前影像,經影像比對方法,如尺度不變特徵轉換 (SIFT, Scale Invariant Feature Transform) 或加速穩健的特徵 (SURF, Speed-Up Robust Features) ,將已知圖庫與待測圖檔做特徵點匹配,將匹配成功的圖檔,使用單應性 (Homography) 矩陣推算出其旋轉及平移的資訊,讓機器人在室內可以確認自己所在的位置。本論文實作SIFT及SURF比對法,比較兩種方法對定位精準度及計算時間的關係。另外,本研究也使用兩側的定位,推算機器人中心點位置,此方法可使推算所得的位置誤差更小,定位更準確。
Recently, Homography-based localization approaches for autonomous robots have been proposed. Basically, the approaches collect landmarks such as indicators and posters in advance and measures geometry information of those landmarks in scenes. When the robot is moving, the current images are matching within the image database by using the matching algorithm, e.g. Scale Invariant Feature Transform (SIFT) or Speed-Up Robust Features (SURF). Then, the Homography matrix can be obtained by the current image and its matching image. The robot pose can also be derived by the Homography matrix.
In this thesis, two Homography-based localization approaches using SIFT and SURF have been implemented respectively and the performance of the two approaches are compared and discussed. In addition, this study also uses two cameras mounted on the robot’s both sides for localization. In the simulation, this method can make the localization more accurate.
中文摘要
ABSTRACT
目錄
表目錄
圖目錄
第一章 緒論
1.1 研究動機與目的
1.2 相關研究
1.2.1 以視覺為基礎的定位方法 (Localization Based on Vision)
1.2.2 以感測器為基礎的定位方法 (Localization Based on Sensor)
1.2.3 以地圖匹配為基礎的定位方法 (Localization Based on Map Matching)
1.3 論文結構
第二章 基於演算法的特徵提取
2.1 簡介
2.2 SIFT原理與理論
2.2.1 尺度空間的極值偵測
2.2.2 特徵點定位
2.2.3 方向的分配
2.2.4 特徵點的描述
2.3 SIFT特徵點匹配
2.4 SURF特徵點提取
2.4.1 積分圖像
2.4.2 用於檢測特徵點的Hessian矩阵
2.4.3 特徵點方向與描述
2.4.4 特徵點描述提取
第三章 基於Homography Matrix作機器人定位
3.1 單應性矩陣(Homography Matrix)
3.2 現實空間與影像平面兩者的關係
第四章 實驗結果與分析
4.1 實驗環境
4.2 圖庫與待測圖檔做特徵點匹配實驗結果與分析
4.3 單一相機實驗定位結果與分析
4.4 兩側相機實驗定位結果與分析
第五章 結論
參考文獻
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