光達具有精準且快速測量大量點位的優點，成為近幾年來新興的測量工具，對要求時效及三維量度之工程測量工作有相當大的幫助。常見的工程測量包括土木建設工程、礦石開採、管線配置、建築古蹟維護、地表或建築物變形監控，如水壩變形監控…等。
若儀器在出廠前，沒有經過嚴謹的校正，或是出廠後，因為使用上的外力碰撞和其他未知的因素造成儀器內部構造的變化，會使得掃瞄結果可能含有系統性誤差，影響定位精度。然而在考量時效及經濟因素上，難以時常將儀器送回原廠商進行校正，因此嘗試發展一套地面光達校正標準作業程序。本研究採用校正場法，嘗試透過校正場設計和選擇適合的數學誤差模式進行儀器校正，藉由校正的作業，在後續內業處理點雲資料前先消除系統性誤差以提高量測精度。
本研究之實施方法是在校正場佈設反射標。反射標的設計需考量所使用材質對雷射光波應具有高反射性質且能夠明確定位。由傳統地面測量方式和地面光達分別對校正場重複觀測，兩觀測數據透過三維剛體轉換至相同坐標系，分析其坐標差，再利用六參數、七參數或六參數加上附加誤差參數同時進行平差計算，找出地面光達通用的數學誤差模式，改正系統性誤差，藉由此數學誤差模式，找出儀器確實存在之誤差來源。
本實驗完成建置室內及室外兩座校正場，待檢校地面光達經校正後，後驗精度明顯變佳，各標心誤差小於儀器規格提供之對點精度數量皆增加，說明本實驗所提之校正方法為可行。

Ground-based LiDAR(Light Detection and Ranging) has became a novel instrument to applications of engineering because of its advantages of high accuracy and efficiency for 3D measuring. Related applications include civil engineering, building reconstruction, deformation monitoring, architecture maintaining, and so on.
The collected data, called point clouds, may introduce systematic errors if the LiDARs are not well calibrated, which would influence the positioning accuracy. Although sending the instrument back to the factory for calibration is a way to solve the questions, it is inefficient and uneconomic. In this paper, we propose a standard procedure of ground-based LiDAR calibration. By the designed calibration field, the mathematic model of systematic error would be established and applied to eliminate possible systematic errors.
The indoor and outdoor calibration fields are established by attaching reflecting targets on walls and pillars of buildings. The 3D coordinates of targets are derived from both total station and ground-based LiDAR measurements, and transform into union coordinate system through 3D coordinate transformation. The adjustment of six-parameters and seven-parameters transformation with additional error parameters would be employed to model possible systematic errors to find out error sources.
To analysis RMSEs of adjustment, the experiment result shows that the accuracy can acquire improvement after calibration. It proves that the proposed method is feasible and gets the expected result.

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