International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part BS. Istanbul 2004 
Table 1: Results for repeatability check for the case of the baseline targets 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Standard deviation of mean Standard deviation of position of radiometric Mean of absolute differences 
position (m) centre (m) (m) 
target X Y Z Xrad Yrad Zrad Rmean (DX) (DY) (DZ) 
1 | 2.30E-04 | 4.41E-05 | 3.35E-04 | 2.00E-04 | 1.56E-04 | 4.03E-04 | 1.34E400 0.0007 0.0004 0.0010 
2 | 833E-05 | 833E-05 | 2.73E-04 | 7.26E-05 | 1.41E-04 | 3.00E-04 | 1.30E+00 0.0006 0.0007 0.0007 
3 |833E-05 1.13E-04 | 1.05E-04 | 1.20E-04 | 2.60E-04 | 1.48E-04 | 1.89E+00 0.0007 0.0006 0.0015 
4 | 2.19E-04 | 2.11E-04 | 2.00E-04 | 3.69E-04 | 3.10E-04 | 2.15E-04 | 4.27E+00 0.0004 0.0013 0.0024 
mean | 1.54E-04 | 1.13E-04 | 2.28E-04 | 1.90E-04 | 2.17E-04 | 2.67E-04 | 2.20E+00 0.0006 0.0007 0.0014 
  
Table 2: Results for repeatability check for the case of the targets on the wall 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Standard deviation of mean Standard deviation of position of radiometric Mean of absolute differences 
position (m) centre (m) (m) 
target X y Z Xrad Yrad Zrad Rmean (DX) (DY) (DZ) 
1 5.16E-05 1.43E-04 | 4.83E-05 | 4.83E-05 | 5.16E-05 | 8.76E-05 | 1.46E-01 0.0127 0.0124 0.0038 
2 | 1.40E-08 | 8.76E-05 | 3.16E-05 | 0.00E+00 | 4.71E-05 | 5.27E-05 | 1.37E-01 0.0091 0.0007 0.0024 
3 | 4.22E-05 | L49E-04 | 4.71E-05 | 3.16E-05 | 1.43E-04 | 1.05E-04 | 2.29E-01 0.0033 0.0099 0.0021 
4 | 0.00E+00 | 9.49E-05 | 7.89E-05 | 4.83E-05 | 6.75E-05 | 1.06E-04 | 2.22E-01 0.0086 0.0070 0.0022 
S | 3.16E-05 | 1.14E-04 | 8.23E-05 | 4.22E-05 | 5.68E-05 | 9.94E-05 | 4.10E-01 0.0099 0.0068 0.0019 
mean | 2.51E-05 1.1SE-04 | 5.77E-05 | 3A41E-03 | 7.32E-03 | 902E-05 1 229E-0] 0.0087 0.0074 0.0025 
  
  
In each scan, the mean X, Y and Z values were calculated for 
each of the targets. Also, in order to evaluate the repeatability 
of the reflectivity, the mean value and standard deviation were 
calculated. Another part of the process was the calculation of 
the radiometric centre of each target i.e. the weighted mean X, 
Y and Z values, using the reflectivity as a weight. Using the 
derived mean values, the standard deviation was calculated for 
each one of the targets. Furthermore, in order to see how the use 
of reflectivity values implemented in the calculations affects the 
results, the mean absolute difference of the mean and the 
weighted mean values were calculated in each case. These 
calculations, though fairly simple, provide an efficient way to 
evaluate the repeatability. 
Table 1 shows the results from the target data collected at the 
baseline. In Table 2 the results for the case of the targets on the 
wall are given. The small standard deviation in both cases 
indicates that the repeatability of the scans is very high. 
Regarding the mean absolute differences, in the first case they 
appear to be rather small. This can be attributed to the fact that 
the acquired point clouds for each one of the targets were 
trimmed before any computations, so that the remaining points 
would describe only the target. However, this was not the case 
for the targets on the wall. The whole area that was scanned for 
each one of the targets was exported. This resulted in 
differences of a few millimetres, especially along the X and Y 
directions. 
The above results indicate that the repeatability of the 
measurements is very high and that the reflectivity should 
definitely be used in order to identify the centre of the target. 
3. ALGORITHM PRESENTATION 
When Cyrax retroreflective targets are available, it is possible 
to define the position of their centres using the proprietary 
software. However, this is possible only during the data 
collection stage because of the way that this process is 
implemented. Specifically, the scanner acquires the data needed 
for defining the centre of the target after the user has selected a 
point near the actual centre of the target using the viewer of the 
software. The scanner then performs a dense scanning around 
the depicted position. A grid of 38x38 points is created and the 
centre of the target is defined using these data. The density of 
the scan data at this stage is found to be of approximately 1mm. 
However, the way that the centre of the target is defined 
remains unknown. 
Although not very well documented, the topic of automatic 
target identification has been previously addressed in the 
literature (Gordon et al., 2001; Lichti et al., 2000)]. In Lichti et 
al. (2000) three different methods are described. The first 
defines the centre of the target as the position with the 
maximum radiance. The second defines the centre by the mean 
position of the radiometric centre of the 4 strongest returns. The 
third algorithm defines the centre of the target as the 
radiometric centre of all returns. These methods will be referred 
to henceforth as ‘maxrad’, “maxrad4’ and  'radcent, 
respectively. In the following experiments, these methods will 
be applied and used for comparison purposes with the new 
developed methods. 
All the aforementioned methods have significant flaws that are 
not mentioned in the literature. The methods ‘maxrad’ and 
‘maxrad4’ often fail because the position with maximum signal 
strength does not always correspond to the actual centre of the 
target. This is clearly shown in Figure 1 which shows part of a 
target with three different markers indicating the position of the 
centre as calculated using each of the three aforementioned 
algorithms. The red points correspond to points of the target 
with a relatively large value of reflectance. They also show the 
topographic artefacts that are observed for the highly reflective 
areas of a target. In Figure la, a front view of the target and the 
calculated centres is given, whereas in Figure 1b, the same 
target is presented from a different angle for visualisation 
purposes. 
In both figures, the ‘maxrad’, ‘maxrad4’ and ‘radcent’ positions 
of the centre are indicated in black, green and blue respectively. 
It is obvious that the 'radcent' algorithm has the best 
performance in this case. This was also confirmed by several 
experiments that were conducted and will be presented in the 
following section. 
   
  
   
  
   
   
  
  
   
  
  
  
   
   
   
  
   
  
  
    
   
  
  
  
  
  
  
  
  
  
  
  
   
  
   
  
   
  
    
  
  
  
  
  
  
   
  
  
   
  
  
   
  
  
  
  
  
   
  
   
  
   
   
   
Interna 
  
  
Figi 
viev 
poir 
area 
max 
indi 
EE dada d à à 
  
Thorou 
parts ol 
the tar; 
the sur! 
the oth 
lower r 
the sur 
determ 
critical