## 5 Discussion and Conclusions  
[PERSON], in his research [15; 17] proposed a methodology of fusing image and laser scan data from the same instrument, such as a MS, to create an RGB+D image and use the data for displacement monitoring. Using computer vision algorithms such as SURF, BRISK, ORB and KAZE, distinct features of an image can be detected and tracked through different measurement epochs. Subsequently, their 3D coordinates can be determined from RGB+D images. This acts as a substitute for discrete geodetic points for further classic deformation analysis. The proposed methodology was objectively and numerically tested within the scope of this research. The authors of this paper have developed a computer code, used for data processing, independently of the original author's code to test the methodology's validity objectively and numerically. After the methodology was validated by implementing the code on test data measured with a Trimble SX10 MS, the next step was to test the achievable precision numerically. Tests of different image matching algorithms showed that algorithm selection is highly scene dependent. Based on test results, ORB algorithm was shown to be the optimal algorithm for this test case, with good computing performance, the lowest processing times and the highest number of usable features detected in the ROI. ORB was implemented in the final code, as the most optimal algorithm for this use case. Reference data for testing the height displacements calculated from RGB+D were from a numerical model of the beam, modeled based on LVDT measurements, beam geometry, and point cloud data. Model data was built, to a degree, using the point cloud data, which made the model partially dependent on the data. Point cloud data were used for precisely determining the spatial position and orientation of the test beams, which is needed for the model to be in the same local coordinate system as the SX10. Because of the correlation of the model data with the point cloud data, model data can only be used to determine the precision of the image matching and fusion part of the RGB+D method. Due to correlation, model data cannot be used to determine the absolute accuracy of the RGB+D method. The residuals (differences between RGB+D height and reference height obtained by LVDT) were distributed normally with some outlier elements, as shown in Figures 15 and 16. suggesting that there was a negligible influence of systematic errors in the measurements and that the dominant factor was noise. Standard deviations for both epochs were below 1 mm with standard deviation error below 0.1 mm, as shown in Tables 5 and 6. From the shown data, it can be concluded that the precision of the image matching and fusion part of the RGB+D is \(\pm\)1 mm, for this test case, with a GSD of 0.3 mm. The calculated achievable precision for determining height displacement is \(\pm\)2.7 mm. Conducted tests and achieved results show the applicability of the RGB+D method for SHM using geodetic instruments such as multi stations. RGB+D method provides the ability to track multiple discrete points on the testing element through the testing, that only point clouds and relative measurement methods cannot provide. Thus, providing new data for testing the validity of behavior numerical models of an element being tested. Based on the positive outcomes of conducted tests, further research in the real-world conditions on man-made structures is needed to prove the validity of RGB+D for the purpose of SHM. Further tests are planned to determine the relation between the GSD and achievable accuracy and precision. Conceptualization, J.P. and R.P. ; methodology, J.P., R.P. and V.D. ; software, J.P. and V.D. ; validation, R.P. and B.K. ; formal analysis, J.P. and V.D. ; investigation, J.P. and V.D. ; resources, J.P. and V.D. ; data curation, J.P. and V.D. ; writing--original draft preparation, J.P.; writing--review and editing, J.P., V.D., R.P. and B.K. ; visualization, J.P. and V.D. ; supervision, R.P. and B.K.