The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
1042 
j) 
n) 
a 0 [10' 3 mm] 
a, [IO 3 ] 
b, [10‘ 3 rad ] 
b 5 [10' 3 mm] 
Cj [10' 3 mm] 
C; [ 10" 3 rad ] 
4.00 ±2.23 (90%) 
1.60 ±0.64 (98%) 
2.91 ± 1.79 (80%) 
1.50 ±0.51 (99.0%) 
2.86 ± 1.02 (99.0%) 
0.85 ±0.09 (99.9%) 
5.09 ±2.18 (98%) 
1.37 ±0.63 (95 %) 
1.94 ± 1.54 (-) 
1.58 ±0.46 (99.9%) 
3.97 ±0.88 (99.9%) 
0.97 ±0.08 (99.9%) 
8.007 ± 0.002 
-153.7 ±1.3 (99.9%) 
-75.2 ±1.8 (99.9%) 
1) 
c [mm ] 
x 0 ‘ [ 10' 3 mm] 
y 0 ‘ [ 10' 3 mm] 
8.011 ±0.006 
-153.9 ±1.2 (99.9%) 
-72.7 ± 3.7 (99.9 %) 
Table 5. Estimated additional calibration parameters of laser 
scanner and fisheye lens camera 
Own independent investigations have shown that the laser 
scanner calibration values vary due to different measurement 
conditions (distance range, scan resolution, target design, etc.). 
Therefore it is reasonable to implement the self-calibration 
strategy into the laser scanner processing in order to obtain 
values, which are particularly effective under the measurement 
conditions at hand. Thus, the accuracy of the laser scanner data 
can be improved in general. 
4.6 Variance component estimation results 
Table 6 shows the estimated a-priori standard deviations of the 
observation groups as a result from the bundle adjustment with 
variance component estimation (Schneider & Maas, 2007) of 
example j), 1) and n). The observations are separated in distance 
D, horizontal and vertical scan angle a, f and image coordinates 
x y ’. These values provide information on the accuracy of the 
observations, which depends on the accuracy and stability of 
the used instrument, on the measurement conditions as well as 
on the correctness of the geometric model used for the 
calculation. 
j) 
1) 
n) 
D 
8.75 mm 
8.68 mm 
a 
15.0 mgon 
- 
14.9 mgon 
ß 
15.3 mgon 
- 
15.1 mgon 
x ’’y’ 
- 
0.228 pixel 
0.176 pixel 
Table 6. Estimated variance components of observations 
It can be seen that the estimated standard deviations of each 
observation group were slightly improved in the integrated 
processing. The reason of this reduction is the higher reliability 
of the results due to different types of observations which are 
able to control each other within the bundle adjustment, i.e. 
outliers can be detected easier. Therefore a few more observa 
tions have been identified as outliers in calculation (n) in com 
parison to (j) and (1). This fact causes an improvement of the 
standard deviation of the observations as well as a slight impro 
vement of the standard deviations of the unknown parameters. 
5. CONCLUSIONS 
Terrestrial laser scanner and fisheye lens camera complement 
one another quite well in an integrated processing scheme. 
Application-wise, a terrestrial laser scanner is mainly used for 
3D modelling by an object representation based on stochastic 
distributed points, while a camera image is used for coordinate 
determination of discrete points as well as colorization of laser 
scanner point clouds or texturization of 3D models. 
The simultaneous bundle adjustment of laser scanner and 
fisheye image observations as presented in this paper provides 
numerous advantages. One advantage of this approach is, that 
the camera can be orientated and calibrated on-site, which 
promises an optimal registration between both data sets. 
Furthermore, the camera can not only be used for providing 
colour information, but it is also able to participate in the 
determination of object geometries in terms of coordinates of 
object points in a multi-station configuration. Depending on the 
image resolution and camera stability, the camera even has the 
potential to improve the accuracy of 3D object points in 
comparison to the pure laser scanner measurement and to 
support the self-calibration of the laser scanner and thus to 
increase the accuracy of the laser scanner point cloud in general. 
Due to different types of observations used in one calculation 
process, the reliability of the parameter and coordinate 
determination can be enhanced. The observations control each 
other, resulting in improved outlier identification. 
Strictly spoken, the results and the drawn conclusions presented 
in this paper only apply to the actual recording and analysis 
parameters (scan resolution, sub-pixel image measurement of 
signalised points, etc.). Nevertheless, the potential of the 
presented approach (in terms of instrument calibration, sensor 
registration, enhancement of accuracy and reliability) has been 
shown. 
In practical applications it is recommended to choose the laser 
scanner positions according to optimal visibility of the object 
details without occlusions and to capture a few fisheye images 
additionally, either from the same position as the laser scanner 
(if the camera is mounted on the laser scanner) or from different 
positions allowing for an optimal intersection geometry. 
Finally it has to be noted that these conclusions also apply for 
conventional central perspective images, but with the limitation 
of a smaller field of view in comparison to fisheye images. 
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