中文摘要
　　光達觀測所得到的是高密度點雲資料，具有詳細記錄地表以及物體表面資訊之特性，然而完整的觀測常需要透過多測站或多航帶的掃瞄，於是產生多測站或多航帶點雲資料結合問題。透過重疊點雲資料之共軛點位進行地面光達點雲資料的結合是常用的方法，而空載光達的應用亦常需以共軛點位進行航帶平差。由於點雲資料並非規則的分佈，亦無直接共軛的掃瞄點，必須透過點雲資料的分佈情形來決定共軛的位置。而且點雲資料具有三維分佈的特性，以傳統影像匹配的理論並不適用，因此本研究之目的，在於以符合點雲三維分佈特性之三維規則網格結構概念，對點雲資料進行匹配，以其自動化獲得點雲之共軛點位。

　　本研究將點雲資料所處空間進行三維規則網格切割，並以三維的標準化互相關(NCC)匹配法進行點雲的匹配工作。計算得搜尋空間的NCC值後則利用動差之統計特性，分別以一階原點動差以及二階中心動差，對三維分佈的NCC值進行定位以及匹配方向性的評估。如此則可利用一階原點動差決定匹配的位置，而以二階中心動差界定匹配的準確度。

　　實驗中所使用的點雲資料包括， Optech ILRIS-3D地面光達的建物掃瞄資料，以及Leica ALS 40空載光達系統之空載光達點雲資料。經實驗顯示本研究所提出之方法，除可成功地將地面以及空載之三維網格化點雲資料作匹配外，以二階中心動差評估匹配之方向性，則提供一項具方向性的匹配成果指標，使得三維網格點雲資料之匹配，在將來的應用上更具潛力。

Abstract

　　Point cloud data collected by using scanners records the surface information of scanned objects. A complete observation is frequently composed of several scans, so that how to merge multi-scanned data sets becomes an important issue. Finding conjugate points in the overlapped parts of scanned data sets and calculating the coordinate transformation parameters are the common steps of merging point cloud data. However, the distribution of point cloud is not regular, so that there are no direct corresponding points. A conjugate point has to be derived through a match or an analysis of the distributions of points between the conjugate areas. This thesis presents a point cloud matching method to find conjugate points for Lidar data.

　　The proposed matching method works based on a 3D regular grid structure data which can be obtained by interpolating the point cloud data into a 3D grid. Therefore, 3D Normalized Cross-Correlation Matching (NCC) can be applied. The matching position and matching quality can be estimated by analyzing the NCC coefficients. The first order original moment of NCC coefficients are used to estimate the matching position, and the second order central moments of the NCC coefficients are used to estimate the quality in each direction.

　　The test data applied in this research includes a set of airborne laser scanning data and a set of ground laser scanning data. The effects of grid size and the use of intensity data in the matching process were analyzed. The experimental results show that 3D grid structuring point cloud data could be matched successfully, and matching quality can be estimated by using the second moments of NCC coefficients.

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