The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
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• Measurement of the coordinates of 15-20 marked points per 
each target. The points were measured manually with a 
precise reflective prism and Trimble 3601 DR total station. 
In addition, other targets located on the buildings and 
visible in Figure 1 were measured in order to guarantee a 
set of tie points that link the TLS with the topographic data. 
• Movement of the 10 targets, imposing displacements with 
different magnitudes (approximately between 19 and 60 cm) 
and directions. 
• Repetition of the steps 1 and 2. 
• Estimation of the 6 parameters of deformation for each 
target by using the proposed TLS approach: Global 
matching over the stable areas (the scene shown in Figure 1, 
with the exception of the 10 targets) and Local matching 
over each target. 
• Independent estimation of the 6 deformation parameters of 
for each target by using the topographic data. 
• Comparison per each target of the two independently 
estimated sets of 6 parameters of deformation. 
• Analysis of the results. The outcomes of the analysis are 
discussed below. 
Figure 3: Data used in the global matching of the point clouds. 
3.1 Validation results 
Once the experiment was done the comparison between the 
results coming from the proposed TLS approach and the results 
coming from topographic survey was performed. The 
topographic results are treated here as the real values here since 
their precision is assumed to be one magnitude better than the 
precision of the results coming from TLS. The differences 
between the TLS estimations and the estimations coming from 
topography represent the TLS errors. 
Results are compared for both 100m and 200m datasets. For this 
analysis it have been used only the translations. More detailed 
analysis can be seen in Monserrat and Crosetto (2008). The TLS 
errors for the three estimated movement components (X, Y and 
Z) are shown in Table 1. Note that due to some occlusions that 
had place in the area in the dataset acquired from 200m only 8 
targets were measured. 
The validation results of the 100 m dataset are summarized 
below: 
100m 
T1 
T2 
T3 
T4 
T5 
T6 
T7 
T8 
T9 
T10 
X 
0.6 
0.2 
0.8 
0.6 
0.3 
1.1 
2.0 
0.5 
0.1 
-0.6 
Y 
0.5 
0.5 
0.1 
0.8 
1.0 
0.9 
1.1 
1.6 
0.2 
-1.3 
Z 
0.3 
0.5 
0.8 
0.4 
1.2 
0.3 
0.6 
2.4 
1.9 
-0.3 
200m 
X 
0.8 
- 
- 
2.4 
0.3 
5.7 
2.7 
2.2 
1.0 
1.1 
Y 
2.3 
- 
- 
1.3 
2.6 
1.0 
1.0 
1.4 
1.8 
-1.8 
Z 
0.4 
- 
- 
0.5 
1.3 
4.4 
1.4 
2.8 
2.9 
-2.4 
Table 1: TLS errors in the estimated deformation displacements 
(X, Y and Z) for the 100 m and 200 m datasets. The errors are 
given in centimetres. 
• in X, 8 of 10 targets have errors with magnitude below 1 cm; 
the mean absolute error over the 10 targets is 0.7 cm, and 
the maximum absolute error is 2 cm, 
• in Y, 6 of 10 targets have errors with magnitude below 1 cm; 
the mean absolute error is 0.8 cm, and the maximum 
absolute error is 1.6 cm, 
• in Z, 7 of 10 targets have errors with magnitude below 1 cm; 
the mean absolute error is 0.9 cm, and the maximum 
absolute error is 2.4 cm. 
The 200 m dataset has the following characteristics: 
• in X, 2 of 8 targets have errors with magnitude below 1 cm; 
the mean absolute error over the 8 panels is 2 cm, and the 
maximum absolute error is 5.7 cm, 
• in Y none of the 8 targets has an error with magnitude 
below 1 cm; the mean absolute error is 1.7 cm, and the 
maximum absolute error is 2.6 cm, 
• in Z, 2 of 8 targets have errors with magnitude below 1 cm; 
the mean absolute error is 2 cm, and the maximum absolute 
error is 4.4 cm. 
Results presented above indicate significantly better 
performances of 100 m dataset in respect to the 200 m one. 
Secondly it can be also observed that there are no remarkable 
differences between the errors associated with the three 
displacement vector in X, Y and Z. In the following section a 
detailed analysis of the above data is done. 
4. CURVE MATCHING 
The first results of the deformation measurement procedure 
described above were achieved only using the surface matching. 
The procedure has been recently extended to include the curve 
matching procedure proposed in Gruen and Akca (2005). The 
proposed approach involves the extraction of contours from 
objects in the given point clouds and the matching of these 
contours. Below the main steps are described: 
a. Identification of the same objects in the different point clouds. 
b. Extraction of the contour of each object and for each point 
cloud. In this way we get the curves to be matched. This step, 
which is currently performed manually, needs to be 
improved. For this purpose different kinds of edge detectors 
could be used. However, there are limitations to get a fully