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博碩士論文 etd-0108118-161934 詳細資訊
Title page for etd-0108118-161934
論文名稱
Title
影像特徵匹配之水下定位效能評估
Performance Evaluation of the Image Feature-Based Algorithm for Underwater Positioning
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
115
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2018-01-22
繳交日期
Date of Submission
2018-02-08
關鍵字
Keywords
拖曳式載具、特徵匹配、水下定位、海床影像、影像特徵
Seafloor image, Feature matching, Underwater positioning, Image feature, Towed vehicle
統計
Statistics
本論文已被瀏覽 5695 次,被下載 36
The thesis/dissertation has been browsed 5695 times, has been downloaded 36 times.
中文摘要
基線 (Baseline) 聲學定位系統可以提供絕對定位資訊,但佈放與校正耗時繁瑣,定位精度容易受到水文環境隨時間和空間變動的影響,而且定位資訊更新率受水深影響而普遍偏低。都卜勒流速計與慣性導航系統定位資訊更新率較高,但只能提供相對位移,而且積分漂移誤差隨著時間越長而累積變大。考量水下攝影機幾乎是水下載具的基本配備,具有低成本、高解析度和高更新率的優點,若能利用攝影機拍攝的海床影像來估算水下載具位移資訊,進一步與絕對定位的基線系統資料比對,了解水下影像定位的可靠度。因此,本研究採用國立中山大學海下科技研究所開發的深海拖曳式光纖探測系統 FITS (Fiber-optical Instrumentation Towed System) 於台灣西南海域千米水深所拍攝的海床影像進行定位效能分析,先由海床視訊擷取出影像,再藉由影像特徵偵測與匹配演算法估算水下載具相對定位資訊。考量影像特徵匹配的效能受到許多因素影響,例如影像特徵豐富度、特徵偵測演算法、Hessian閥值制定方法、影像匹配時間間隔以及影像對比度,因此本研究探討與分析不同因素對於影像特徵匹配之水下定位效能的影響。影像特徵匹配之定位結果則與FITS搭載之DVL定位結果互相比較,做為影像特徵匹配定位效能之評估依據。
Abstract
Baseline acoustic positioning systems can obtain the absolute positions of underwater targets, but its update rate is slow and calibration is often complicated and tedious. In addition, temporal and spatial variations of water column sound speed profile significantly affect positioning accuracy of the baseline system. Doppler velocity log (DVL) and inertial navigation system (INS) have higher update rate than the baseline systems. But DVL and INS are dead reckoning systems which provide relative positioning only and suffer from time-dependent drift error. Considering that video cameras are standard equipment on almost underwater vehicles, it is easy to collect seafloor videos for a vehicle while conducting seafloor survey. With the advantages of high resolution and high frame rate, the seafloor video has great potential for accurately positioning an underwater vehicle based on image feature detection and matching. Therefore, in this study, the feature-based image matching algorithm for positioning an underwater vehicle is proposed. The seafloor videos collected off southwestern Taiwan at a depth of about 1000 meters by the deep-towed vehicle FITS (Fiber-optical Instrumentation Towed System) are used for evaluating the performance of the proposed algorithm. As the success of image feature detection and matching depends on various factors such as seafloor richness and roughness, descriptor of feature detection algorithms, threshold for Hessian keypoint detector, overlapping area of two images, and illumination, considerable effort in this study was made to assess the effects of various factors on the performance of the proposed algorithm. Performance evaluations were conducted by comparing the estimates of the proposed algorithm to the measurements of the DVL onboard the FITS.
目次 Table of Contents
第一章緒論+1
1.1 研究背景+1
1.2 研究動機與目的+3
1.3 論文架構+5
第二章研究方法+7
2.1 影像特徵匹配定位流程+7
2.1.1 鏡頭徑向扭曲校正+8
2.1.2 影像平面姿態校正+10
2.1.3 影像尺度轉換+13
2.2 定位效能影響因子+16
2.3 定位效能評估方法+22
第三章特徵物豐富度+28
3.1 特徵物豐富度鑑別+28
3.2 定位效能評估與討論+33
3.3 小結+40
第四章特徵偵測演算法和Hessian 閥值制定+41
4.1 特徵偵測演算法+41
4.2 Hessian 閥值制定+45
4.3 小結+51
第五章影像匹配時間間隔+55
5.1 特徵物貧瘠區+56
5.2 特徵物豐富區+61
5.3 小結+65
第六章影像對比度+67
6.1 定位效能評估+69
6.2 風險討論+76
6.3 小結+81
第七章結論與討論+82
7.1 結論+82
7.2 討論+85
參考文獻+86
附錄A 影校平面姿態校正+89
附錄B DVL 高度資訊校正+94
附錄C 直方圖均衡化+96
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