地面光達可以快速獲得高精度的三維點雲資訊，各掃描站的點雲資料參考於區域座標系統，每站的掃瞄範圍有限，通常須結合多站點雲資料才能完整涵蓋場景，因此多站點雲資料需整合到同一坐標系統，才能結合成完整的場景，某些部分應用還必須將點雲轉換到地面座標系統(地理定位)，傳統做法為利用至少三個已知的控制點，求解座標系統之間的轉換參數，進行座標轉換，但控制點的布設降低觀測的效能。車載或空載光達則使用全球定位系統加上慣性測量儀的方式求解(直接地理定位)，但價格昂貴。
目前地面光達可以讓使用者裝載GPS天線，透過GPS的觀測，即可獲得建立在地面座標系統下的座標，本研究利用GPS天線、地面光達掃描中心和地面特徵點之幾何關係所組成的觀測方程式，進行多站點雲聯合平差，求解掃描儀位置和姿態，完成點雲結合及地理定位，不需使用地面控制點。
本研究於兩個測試場進行實驗，一個為國立成功大學的自強校區，另一個為億載金城，兩個實驗資料皆利用Riegl的商業軟體及本研究使用的方法處理並進行比較，在自強校區測試場中，我們選擇牆面磁磚交界之特徵點作為檢核點，所有檢核點皆經過嚴密的地面測量求得其座標，在不使用地面控制點進行聯合平差的成果精度在E、N、H方向分別大約為2、2、7公分。在億載金城測試場中，其場景較為複雜，總共架設30站完成資料蒐集，不使用地面控制點進行聯合平差的成果精度在E、N、H方向分別約為4、4、7公分。從實驗結果可以證實，加入GPS觀測量進行多站聯合平差，可以在不使用地面控制點的情況下進行地理定位，其精度足夠做為應用。

Terrestrial LiDAR is the state of the art technology of collecting 3D spatial data. Point clouds delivered by a terrestrial LiDAR are referenced to a local coordinate system defined by the laser scanner. For most applications, raw point clouds should be transformed into a global coordinate system, i.e., geo-referencing of point clouds, to match the need for practical applications. The geo-referencing of terrestrial LiDAR data is currently relied on known control points. However the acquisition of control points for geo-referencing is often troublesome and laborious since the field surveying is usually required for obtaining very high accuracy control points. A direct geo-referencing system, which is necessarily employed in a mobile LiDAR system, is certainly too expensive to the use of a terrestrial LiDAR. However, it is quite common for a modern terrestrial LIDAR to have a mount for GPS antenna. It means that the precise position of a scan station can be obtained in the field. Under this circumstance, geo-referencing of terrestrial LiDAR data can be achieved by combining several (at least three) overlapping point clouds scanned from multiple stations. This is the idea motivates this study.
This paper presents a methodology to achieve geo-referencing with few or even no control points. In our strategy, we utilize the observation equations based on the geometric relationship between the center of GPS antenna, terrestrial LiDAR, and point features to perform multi-station adjustment. For the experiments, there are two test field involved, one is a well-controlled test field in the Tzu-Chiang Campus of National Cheng Kung University, and the other case is Eternal Golden Castle in Tainan city. The test data of these two cases were processed with the manufacture software, and the proposed network adjustment model. In the Tzu-Chiang Campus case, we tested three conditions of network adjustment, which are adjustment with control points only, with GPS positions only, and with both of them. The results show that the accuracy of check points in network adjustment with GPS observations only can achieve about 2 centimeters in E and N directions, and about 7 centimeters in H direction. In the Eternal Golden Castle case, where the landscape is much more complicated. The accuracy of check points in network adjustment with GPS observations only can achieve about 4 centimeters in E and N directions, and about 7 centimeters in H direction. The results show that the accuracy of network adjustment with GPS observations only are quite well enough for applications.

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