臺灣博碩士論文加值系統

目前大多數的地板區域偵測演算法仍然依賴著深度或一些外加的感測器，相對的需要消耗更多的計算資源並且增加無人地面載具的總重量。在本論文中，提出了一個創新的架構，讓無人地面載具能夠僅使用單張影像輸入來判斷出地板區域。由於在室內環境中有花紋地板、陰影和反光，使得區分地板區域更加困難，因此我們提出的演算法結合了從表面紋理特徵提取以及從物件邊緣或是線段中找出特定的幾何區域，透過分類器來區分出地板區域與非地板區域，並使用ARM-FPGA異構計算平台來實現硬體加速使得演算法達到即時計算的效能。在實驗結果中，我們使用public MIT Scene dataset和各種室內環境的數據集來驗證我們提出演算法的正確率，而我們提出的演算法在沒有外加任何感測器情況下，正確率平均可達到94.72%。

By far most of the floor regions detection algorithms are still dependent on the depth or other additional sensors that consume more power relatively and increase the total weigh for smart land rovers. In this paper, a new framework that enable to estimate floor regions in single image for Unmanned Ground Vehicle (UGV) robots is presented. In general case, the cluttered indoor surroundings such as patterned floors, shadows and reflections, those surroundings are very difficult to identify floor regions. The proposed algorithm combines extracting surface texture characteristic with specific geometric area is able to find out from object boundary, and through SVM to distinguish between floor and non-floor regions. In order to achieve real-time performance, the proposed algorithm has been implemented on an ARM-FPGA heterogeneous computing platform with hardware acceleration methodology. In experimental results, public MIT Scene dataset and indoor database were selected to verify the detection accuracy. The proposed algorithm accuracy can reach up to 94.72% in average without any other sensors for assistant.

摘要......i
Abstract......ii
誌謝......iii
目錄......iv
表目錄......vi
圖目錄......vii
第一章 緒論......1
1.1 研究背景與動機......1
1.2 研究目的與方法......1
1.3 文獻回顧與探討......1
第二章 地板區域偵測演算法......6
2.1 表面紋理特徵提取演算法......6
2.1.1 超像素演算法......8
2.1.2 提取超像素紋理特徵......11
2.2 圖片特定幾何區域......13
2.2.1 Canny邊緣偵測......13
2.2.2 線段交叉點偵測......16
2.2.3 物件標籤化......20
2.3 地板與非地板區域分類......24
2.4 特定表面地板區域......26
第三章 演算法實驗結果......28
第四章 嵌入式系統應用與機器人載具實現......34
4.1 嵌入式系統應用......35
4.1.1 RGB轉CIELAB硬體加速實現......36
4.1.2 LMF硬體加速實現......37
第五章 結論......39
參考文獻......40
Extended Abstract......42
簡歷(CV)......47

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