## 5 Summary and Conclusions  
Due to the wide use of image matching techniques in the construction industry, and the vulnerability of these techniques to correctly detect and match scene features when extreme transformations in images occur, this study aimed to investigate how to reduce image transformations. For this purpose, different scenarios in which image transformation can take place were visualized. It was shown how these transformations could occur when the position and orientation of a camera change in three linear directions and three angular orientations. As was illustrated, to reduce image transformations, changes in the viewpoint (i.e., position and orientation) of the camera needed to be reduced. For this purpose, different techniques were reviewed, and the most accurate one was selected. This technique included positioning sensors that worked based on UWB waves, and orientation sensors such as acceleration, magnetic, and angular velocity. To apply these sensors for the purpose of reducing image transformation, a system architecture was defined, and a prototype was developed. The development of the prototype included two phases. In the first phase, the positioning and orientation modules (i.e., tag and anchors) were integrated with a tablet camera such that these sensors could detect any change in the position and orientation of the camera. In the second phase, a user interface was created to display information regarding the position and orientation of the camera such that users could monitor the location and viewpoint of the camera. To compare how using the sensor-based approach could be different than a non-sensor-based approach, in terms of decreasing changes in position and orientation of the camera, an experiment was designed and conducted. The experiment included two tasks. For the first task, the participants were asked to use their common sense to capture the best picture possible from a scene. For the second task, they were asked to capture a picture from the same scene but with the assistance of positioning and orientation sensors. The images participants generated for these two tasks were evaluated in terms of accuracy (i.e., producing pictures that resemble the reference picture), and precision (i.e., producing pictures that resemble each other). The results of the experiment demonstrated that when participants used the sensor-based approach, a significant reduction in accuracy errors in the X and Y directions, and also the precision error in the Y direction, was achieved. The precision error in the X direction was slightly higher when the participants used the sensor-based approach. Regarding the orientation, the average results for both approaches did not show a significant difference. While accuracy error was slightly better for the non-sensor-based approach for pitch, it was slightly worse for yaw, and the same for roll (however, if the two samples with 90 degree rotations were not discarded from data related to the non-sensor-based approach, the error for this approach increased significantly). For the sensor-based approach, precession errors were slightly lower for pitch and roll and moderately lower for yaw. In conclusion, these results showed that applying the sensor-based approach can control the camera's overall position and orientation and reduce image transformation. This can be important for feature detection algorithms used in applications such as augmented reality and change detections that use features of the environment in temporary and messy locations such as construction sites, where using a tripod or fixed-point camera is not possible. This research had technical limitations. The accuracy and precision of the sensor-based approach could improve. For instance, in this experiment, only four anchors were used. Using more anchors and even tags could improve the results. By using more powerful tablets, the time for data processing could be reduced, and the positioning system in the Z direction, which for this experiment was off, would be functioning.