International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
  
(b) Homologue points with 50% resultion (2128 x 1416 pixel) 
Figure 3: Different number of feature points, due to different res- 
olution 
  
| image resolution || number of corresponding feature points | 
  
  
  
  
  
100% 61 
75% 167 
50% 197 
25% 97 
  
  
  
  
  
Table 2: Number of corresponding feature points with different 
image resolutions. Respective image examples Fig. 3 
camera parameters. Outliers are detected and eliminated and all 
residuals are minimized by the method of least squares. As a 
result, generated errors are distributed in equal parts to each cam- 
era position and existing point of views exhibit only small errors. 
Bundler, utilized in the post processing method, uses a sparse 
bundle adjustment. In compare to the sequentially working real 
time approach, the estimated camera position are more accurate, 
but the analysis can only start after the entire whole image record- 
ing is completed. The real time algorithm directly estimates the 
position and orientation after capturing a single image, but the er- 
ror is increasing from image to image because of variance propa- 
gation. 
5.1 Post Processing Approach 
Unfortunately, Bundler provides no accuracy results of obtained 
calculations. Therefore, an external bundle adjustment, which 
imports the results of Bundler (3D points, 2D points, image ori- 
entation) has been used to compute the results with accuracy de- 
tails again(see table 3). All object points from Bundler are em- 
ployed as control points in this bundle adjustment and previously 
obtained feature points are observations; therefore it is more of a 
multi space resection adjustment. 
  
| parameter I mean standard deviation for 36 frames ] 
  
  
  
  
  
  
  
  
X 1.4mm 
Y 1.6mm 
Z 1.8mm 
XYZ 2.8mm 
w 0.010° 
© 0.012° 
K 0.004° 
  
  
  
  
  
Table 3: Mean accuracy of camera orientation from Bundler 
This computation achieved results based on a excellent image ac- 
quisition without any interruptions or difficulties. That means 
there was no interruption of image sequence, no obstacles, con- 
60 
stant velocity and without strong wind. Thus, this mentioned re- 
sults are very optimistic but also possible. Bundler automatically 
computes a high accurate pose estimation based on a small num- 
ber of parameters (focal length and image size). It detects natural 
feature points with an accuracy below one pixel in image space 
and therefore it can employed in forest applications as well. The 
obtained results are encouraging and demonstrate that the post 
processing method is indeed feasible. PMVS provides a patch- 
based algorithm that uses the results from Bundler to create a 
dense point cloud of environment. Based on a extended multi- 
view patch correlation, PMVS increases the number of points to 
four times in compare to Bundler with given image sequence of 
36 frames. 
  
(a) Bundler results; camera position and orientation and computed 3D 
points 
  
(b) PMVS2 results; dense point cloud 
Figure 4: results of Bundler and PMVS2 
5.2 Real Time Approach 
As presented in table 4, the mean accuracy is comparable with 
the post processing method (section 5.1). However, illustrated re- 
sults are just the accuracy values for one image pair. Because of 
variance propagation, the uncertainty of all following images in- 
creases. It is noticeable, that rotation values (w,9,4) are worst in 
comparison to translation. The basic cause for this effect is a clus- 
tered tie point allocation: In some images, homologous points are 
very close together and concentrated in only one region of the im- 
age. As aresult the rotation parameters can not be computed with 
the expected accuracy. 
To avoid the problem of variance propagation, successive image 
have to be connected to each other. Detected and stored feature 
points can be used to obtain the relative orientation between two 
or more images. In that way, preceding images could be linked 
with current images to stabilized pose estimation. One possible 
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