Experimental study is an important methodology for ocean engineering research. However, measuring any physical parameters in the field involves the instrumentation to overcome a wide range of problems, such as robustness to the severe environment, limitation of power supply and data storage, and also simplicity of operation. Taking seabed profile or bedform measurement as an example, conductivity point gauge or ultra-sound non-contact profiler is usually adopted. Limited by the time needed per sampling, these approaches are costly in operation if dense grid points are required to describe the variation of a long transect. In addition to this drawback, the surface will be disturbed after conductivity-based contact probing. We propose using an off-the-shelf, non-metric CCD camera along with a simple calibration methodology as an alternative to carry out the measurement. As the semiconductor technology advances drastically, nowadays high quality CCD cameras are available with inexpensive prices. Recently, CCD camera emerges as a convenient input sensor for many applications. However, generally it requires a delicate optical and geometrical calibration of the camera before it can be used to carry out 2D or 3D measurement of the target. The optical parameters are focal length, distortion of lens, optical axis offset, CCD array linearity and etc; and geometrical parameters are position and orientation of the CCD camera. Some of these parameters are sensitive to the setup of the system, and a re-calibration is needed whenever the system is disassembled or moved. We propose using a template on which grid points of known locations are used to construct several sub-mappings between measurement coordinate system and image pixel coordinate system. This simple procedure is effective to meet the accuracy requirement for several applications. In this work, this idea is adopted and verified in three different experiments: An underwater laser line scanner, a cross-section wave tank bedform profiling and Particle Imaging Velocimetry.

整合了GPS、INS的無人飛行載具(Unmanned Aerial Vehicles, UAVs)，可提供安全、快速的資料蒐集方法，而能執行自動駕駛(automatic pilot)功能的UAV系統，更可提高資料蒐集的自動化程度。資料收集時，UAV系統中的GPS天線、INS系統以及像機的透視中心並不一致，欲以UAV系統執行航測任務時，須先了解UAV的系統幾何與特性，才能從GPS、INS的記錄資料中取得適當的外方位參數參考值。此外，目前的UAV系統多搭載非量測型像機(non-metric camera)獲取影像，但非量測型像機的內方位參數常以近景攝影測量的方式率定而得。然而，能以近景攝影測量方式獲得內方位參數的商業軟體很多，其所使用的函數模式卻未必完全相同，將影響內方位參數的率定成果，若再於空三平差過程中把不同軟體解得的內方位參數視為固定值，將使空三平差的結果產生較大的影像定位誤差。而自率光束法除了可用於近景攝影測量中的像機率定，也能應用於航空攝影測量中，將航測作業中的像坐標系統誤差模式化並加以改正，以提升該次作業的空三平差精度。因此，本研究以較安全的四旋翼UAV系統搭載非量測型像機獲取影像，比較：(1)一般航測方法（即光束法）執行空三平差、(2)使用自率光束法的空三平差、(3)先將所有影像觀測量以熟知的系統誤差模式改正後，再使用自率光束法的空三平差（以下簡稱預改正(pre-corrected)的自率光束法空三平差）所能達到的精度。測試結果顯示：使用預改正的自率光束法空三平差時，使用Brown(1976)與Ebner(1976)兩種附加參數模式，皆可得到最佳的空三平差精度，而使用Brown附加參數模式的自率光束法空三平差精度次之，且均比一般航測方法的空三平差精度佳。但於自率光束法的空三平差過程中使用Ebner的附加參數模式，所得的空三平差精度則最差。 / Unmanned aerial vehicles (UAVs) integrating with GPS and INS provide a safe and fast method for data acquisition. The UAVs which can implement automatic pilot promote the automation of data collection. In UAV systems, the GPS antenna and the INS system are not aligned with the perspective center, so that the GPS and INS records should be revised according to the geometry of UAV systems for exterior orientation references. And the cameras equipped with UAVs are often belonging to the non-metric camera, whose interior orientation parameters can be acquired by close-range photogrammetry softwares. However, there are several different camera models used in the softwares and the interior parameters calibrated by different softwares would not be the same, so that the interior parameters of the non-metric camera should not be regard as constant in aerotriangulation. Self-calibration can not only calibrate the camera in close-range photogrammetry but also model and compensate the departures from collinearity in aerotriangulation to promote the positioning accuracy. This study uses the images acquired from a safe UAV system, a Quadrotor UAV, and compares the results by using different aerotriangulation procedures. In this paper, the optimal accuracy can be obtained by using self-calibration in bundle adjustment with all measurements been pre-corrected for radial and decentering lens distortion. And the suboptumal accuracy can be obtained by using Brown’s (1976) added parameters in bundle adjustment, better than the results of using bundle adjustment. But using Ebner’s (1976) added parameters in bundle adjustment cannot help promoting the positioning accuracy.