近年來，室內行人定位系統逐漸發展起來，大多是根據信號的強弱程度來估測使用者的位置或使用感測器量測行人運動物理量進而推得每個時間點的所在位置完成定位，以解決全球定位系統於室內脫鎖的情況。
本論文期望能藉由整合微軟產品Kinect、雷射測距儀與慣性量測單元等量測資訊，開發出一套以行人背負感測器的三維室內建圖與即時定位輔助系統，在行走間同時建立出三維地圖。通過安裝於腰間的慣性量測單元與雷射測距儀資訊，以延伸式卡爾曼濾波器，修正演算法積分造成的累積誤差，透過方位推估法做到行人即時定位。Kinect感測器可得到彩色影像與深度資訊，彩色影像配合GPU-SURF演算法得到影像特徵點並做匹配以尋找影像之間重疊的部分，並以隨機樣本一致性演算法的除錯機制挑選出正確的特徵點對，最後以奇異值分解計算位移量以建立三維彩色點雲圖像。藉由感測器結合行人定位資訊完成室內三維重建功能，以3D立體地圖的方式呈現視覺化、立體化的效果，使建物資訊更完整的呈現。

In recent years, the technology of indoor pedestrian positioning system has been developed. This technology utilizes sensors received signal strength indicator to determine the user location, or determine the movement of human and measure the distance of walking to solve the problem of GPS out-of-lock.
This thesis presents a real-time simultaneous localization and mapping system based on fusing Microsoft Kinect, Hokuyo Laser scanner and Inertial Measurement Unit sensors, to developing a human-portable 3D localization and mapping system. With the information of an Inertial Measurement Unit (IMU) and Laser Scanner sensors mounted on the waist, the accumulated error of velocity can be corrected by the extended Kalman filter algorithm. Besides, the dead reckoning method is used to obtain the information of location. The system can measure color images and depth images from Kinect sensor, extracting visual features from the color images. The correspondent extracted features are matched with adjacent images with additional RANSAC outlier removal procedure to select correct feature points. Finally the Singular Value Decomposition (SVD) is used to finding the optimal rotation and translation between corresponding 3D points to create dense 3D environment representations. By combining the sensors and information of pedestrian positioning to presents the 3D indoor map building system and stereoscopic map for visualization purposes.

論文審定書 i
誌謝 ii
中文摘要 iii
Abstract iv
目 錄 v
圖 次 viii
表 次 xii
第一章 緒論 1
1-1 研究動機 1
1-2 研究背景 3
1-3 論文貢獻 9
1-4 章節簡介 9
第二章 系統概述 11
2-1 系統簡介 11
2-2 系統架構 12
2-2-1 RGBD感測器 13
2-2-2 慣性量測單元 14
2-2-3 雷射測距儀 15
2-3 作業系統與軟體開發套件 16
2-3-1 Visual Studio 16
2-3-2 OpenNI 17
2-3-3 OpenGL 18
2-3-4 OpenCV 20
第三章 系統實現 21
3-1 RGBD即時定位建圖系統[15][19] 21
3-1-1 特徵點萃取 22
3-1-2 RANSAC（Random Sample Consensus）演算法 24
3-1-3 SVD演算法計算平移與旋轉矩陣 26
3-2 IMU/Laser即時定位建圖系統[9][10] 29
3-2-1 延伸卡爾曼濾波器應用於行人定位 30
3-2-2 大地座標轉換 32
3-2-3 雷射測距儀資料處理 36
3-2-4 最近點迭代演算法 39
3-2-5 卡爾曼濾波器模型 42
3-3 多感測器整合建圖系統 44
第四章 實驗結果 51
4-1 定位實驗結果 51
4-2 定位與建圖結果 52
4-2-1 狹窄走道 52
4-2-2 樓層大廳 54
4-2-3 上下樓梯 58
4-3 輪椅平台定位建圖系統 62
4-4 實驗結果討論 64
第五章 結論與未來展望 65
5-1 結論 65
5-2 未來展望 66
參考文獻 67

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