為了促成高品質的光達測量或提供其它測量方法多測站獲取的物表面三維點雲重建高品質物表面之應用與誤差分析和改正，蔡展榮老師提出一個新構想，稱為“連結體法”。本研究提出其中的連結長方體法，它適用於空載光達測量中不同航帶點雲資料連結之用，期望藉以偵測含大錯的資料點，進而檢測各種系統中相對的系統偏差，達成可靠的資料連結之目的。
本研究主要目的是建立並測試連結長方體法的平差模式。首先根據真實空載光達資料中，點雲在長方體建物面上的分佈情形，及其系統偏差初探的結論，進而推導出連結長方體平差模式。為了簡化模式可行性的測試，模式中屬於系統性偏差的部份，只考量不同航帶之間的高程原點平移，並透過各種模擬資料進行理論精度之分析。
模擬測試成果顯示體元結構的良好幾何強度使得不同的點雲密度及分佈情形或是不同座向的長方體建物的平差成果皆能有不錯的精度與可靠度，點雲和長方體表面的套合精度與模擬的套合精度相當。另外，以多連結體同時進行整體平差亦能求得精度為 ±0.0494m的單一航帶點雲高程原點平移量，其精度約等於點雲高程先驗精度的0.25倍。多航帶真實空載光達點雲的實驗結果顯示，採用連結長方體法，不同於一般常見的點/線/面的連結方法，以連結長方體平差計算方式連結不同航帶光達點雲資料，求得套合精度為 ±0.2132m。根據儀器廠商提供的高程先驗精度約為0.15公尺及平面精度約為0.3公尺，證明連結長方體平差模式應用在真實資料中應為可行。

For achieving high-quality LiDARgrammetry and performing surface reconstruction by 3D point clouds acquired at multi-stations, Dr. Jaan-Rong Tsay proposed a novel idea which is called tie voxel method. One of diverse types of tie voxels is tie cuboid. This thesis proposes the tie cuboid method. This method can be used for multi-strips airborne LiDAR points registration. It is hoped that the blunder LiDAR points and the systematic errors could and must be detected somehow.
The main task of this thesis is to formulate and test the tie cuboid model. Firstly, the spatial distribution and systematic elevation errors of real airborne LiDAR points are checked in order to define the prototype of the tie cuboid model. To simplify the complexity of this study, an airborne LiDAR strip is assumed to only have a shift parameter SZ in elevation. Then, theoretical accuracy and reliability of the tie cuboid method is studied by using simulated LiDAR points.
Test results verify that the tie cuboid method provides good accuracy and reliability for airborne LiDAR point registration, even in the case of improper point distribution on a wall plane. The method exploits all implicit geometric conditions such as coplanarity, colinearity and perpendicularity so that it can provide good geometric strength for 3D point cloud registration. The test results using multiple tie cuboids between two neighboring LiDAR strips show that this method can determine an accurate SZ parameter with the a posteriori standard deviation ±0.0494m=0.25σz , where σz=±0.20m denotes the a priori standard deviation of Z-coordinate of a LiDAR point. Moreover, some tests are also done by using real airborne LiDAR points with a priori standard deviations ±0.15m and ±0.30m in vertical and horizontal direction, respectively. The root mean square distance 0.213m of a LiDAR point to the corresponding plane is achieved. These test results verify the applicability of the proposed tie cuboid method.

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