# Technological Development and Application of Photo and Video Theodolites
## 3 Measurement and Processing Approaches
### IATS Measurement Procedures and Data Processing  
Experimental or in situ measurement procedures and data processing workflow for conducting displacement and vibration monitoring (i.e., structural health monitoring) are shown on Figure 11. The workflow can be fully automatic or semi-automatic. The whole process can be automated using Leica's GeoCOM interface. A user interaction is only required at the beginning to initially aim at the targets to be monitored and to set the camera properties [64]. At the same time, several point targets can be monitored at different areas of structures or Earth surfaces as long as they appear in the camera's FOV. The IATS can record a video or image, depending on the purpose of monitoring. If performing structural monitoring with the aim of determining the natural frequencies and dynamic displacements of structures (e.g., during load tests or everyday traffic), then videos are recorded. If performing the geo-monitoring of the Earth's surface (e.g., landslides), then images are recorded periodically. An expert must set up the IATS and aim at the target, as well as set up the camera parameters. For long-term monitoring where targets are observed periodically, in the first epoch of measurements, the expert must aim at the target and, in subsequent epochs of measurements, the registered positions of targets from the first epoch are used for automated aiming. The movements between targets, represented by their pixels on images, can be computed relative to the first frame/epoch or between consecutive frames/epoch. The latter is not appropriate for structural health monitoring when recording video, because the pixel movements relative to the first frame must be computed by cumulating the consecutive movements between each frame, which results in the propagation of random errors in consecutive movements. For vibration monitoring, i.e., determination of a structure's natural frequencies, a conversion from pixel movements to linear units is not necessary. To eliminate trends in the time series prior to the computation of the frequencies via Fourier transformation, accelerations are computed from the pixel movements. The usage of consecutive pixel movements is preferable to reduce noise in the resulting frequency spectrum [59]. Possible IATS telescope rotations which result in camera rotations must also be considered for highly accurate displacement monitoring. These small camera rotations can occur unintentionally or due to tilts of the whole station. Both effects are measured by IATS, i.e., angle and tilt readings. Correction is achieved by subtracting the camera rotations from the image-based angle measurements to the target. Figure 10: Transformations needed to convert image coordinates into theodolite angles and vice versa [8]. Using the proposed image-based measurements, the detection of object movements perpendicular to the IATS line of sight is possible. To measure movements along the sighting axis, the IATS EDM can be used; alternatively, if available IATS scanner data can be used, i.e., fusion of laser scans and camera data to RGB + D images. This approach combines the advantages of two methods. The distance of the laser scan dense point clouds changes as a function of line of sight and can be easily detected. In contrast, camera image data are most sensitive to displacements perpendicular to the line of sight (i.e., to the viewing direction of the telescope in which the camera is integrated). This way, displacements can be determined by extracting common image features (RGB channel) and tracking them over a series of monitoring measurement epochs. In combination with the  
Figure 11: IATS measurements and data processing workflow for conducting displacement and vibration monitoring (structural health monitoring), modified for this study from [64]. depth channel (D), the determined image features (from RGB + D) represent 3D coordinates, which can be used for the computation of 3D displacement vectors [55].