| 2004
MS-Z210.
1) are after
it spacings
om a point
of the two
| the cases
isi GS200,
egistration
ngs of two
1 the pres-
tion of the
di
da
0.119 |
0.118
0.119
0.118
0.119
0.118
0.119
0.118
0.043
0.061
0.194
0.361
0.194
0.371
:d and real
ts of point
hbourhood
id c are the
s the point
e of a point
yverlapping
good a pri-
1 simulated
ise and two
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B5. Istanbul 2004
(a)
(b)
Figure 5: A building and trees scanned by Mensi GS200.
(a) and (b) are before and after the registration, respec-
tively.
real point clouds from two different scanners. The distance
from a point and the corresponding surface was used as the
error metric of registration. The registration errors for real
point cloud registration were the order of centimetre and
that of a simulated dataset were similar with the standard
deviations of zero-mean Gaussian noise.
Several ways are possible to improve our method. In terms
of execution times, we can modify our method to use dif-
ferent neighbour points for each point depending on the
distribution or the area of the region covered by the point
and the neighbourhood. Regarding threshold values, the
properties of the threshold values used in the proposed
method can be investigated in order to provide criteria for
the selection of the optimal threshold values. Furthermore,
corner points of point clouds can be detected using geomet-
ric primitives that have used in the proposed method and
they can be used as initial samples for the registration. In
addition, the scale of corresponding points may be a good
indication of the quality of sampling for registration.
5 ACKNOWLEDGEMENT
This research was supported by an Australian Research
Council (ARC) Discovery grant DP0342887. The authors
thanks to Mensi for provision of GS200 dataset.
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