The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
(a)Ratio of correct matches (b) Distance from points to scanner 
Figure 7: Compare the matching results with and without 
geometric constraint. 
The resulting position and orientation errors for our method are 
shown in Table 4. In order to evaluate the registration results, 
we compared the planar patch approach (Dold and Brenner, 
2006) with our proposed method using the reference orientation. 
In (Brenner et al., 2008), the orientation by planar patches was 
able to align SP] with SP 10 which have an overlap of 16%. In 
contrast, it seems that the proposed method is more influenced 
by scene contents. The proposed method fails after SP 5a , which 
has a considerably larger overlap of 50%. However, the 
distance between SP[ and SP 5a is 20 m, which is probably 
anyhow meet the distance between two scans one would prefer 
to obtain a dense city scan with few occlusions. 
Pair 
Total 
matches 
Right 
matches 
Right 
ratio 
(%) 
Total 
matches 
Right 
matches 
Right 
ratio 
(%) 
01-02 
301 
116 
38.5 
151 
116 
76.8 
01-03 
130 
30 
23.1 
45 
30 
73.2 
01-03a 
132 
32 
24.2 
49 
32 
65.3 
01-04 
127 
16 
12.6 
35 
16 
45.7 
01-05 
113 
10 
8.8 
25 
10 
40.0 
01-05a 
111 
10 
9.0 
28 
10 
35.7 
01-06 
107 
2 
1.9 
21 
2 
9.5 
02-03 
437 
172 
39.4 
216 
172 
79.6 
03-03a 
1714 
1438 
83.9 
1597 
1438 
90.0 
03-04 
328 
133 
34.5 
162 
133 
82.3 
04-05 
609 
309 
50.7 
342 
309 
90.4 
05-05a 
1476 
1220 
82.7 
1352 
1220 
90.2 
05-06 
235 
194 
82.6 
209 
194 
92.8 
On the other hand, the proposed registration method has the 
advantage of being algorithmically simple and does not rely on 
the presence of planar structures. In addition, the proposed 
method is conceptually simpler and faster. Therefore, if it is to 
be preferred depends strongly on the application. As far as 
accuracy of proposed method is concerned, one can see that the 
maximum deviation from the reference is less than 10cm in 
translation, and 0.2° in orientation. This is better than the planar 
patch method which achieved a maximum deviation of less than 
20cm in translation, and 0.5° in rotation angles. 
Considering the required computation time, SIFT feature based 
matching took an average of 20s. In the case of computing 
Gaussian and mean curvature, it is quite time consuming to 
generate the full scene of curvature map. In practice, we only 
compute the Gaussian and mean curvature in the neighborhood 
of matched points. This is very quick and takes around 5s. Then 
a standard RANSAC will take another 5s. Thus, in total, 
approximately 30s were required on average to match two scans 
on a 2GHz Pentium laptop. 
6. CONCLUSIONS AND FUTURE WORK 
We have shown a key point based automatic method using 
intensity and geometry features for the marker-free registration 
of terrestrial laser scans. The method uses SIFT feature based 
key points extracted from the normalized reflectance image 
with geometric constraint. Results and the analysis show the 
proposed method’s efficiency and robustness. The method can 
be used to register laser scanning data at accuracy comparable 
to that of manual registration using natural tie points. 
For the future work, several issues are worth investigating. 
Our approach applies the SIFT method to extract feature 
points. Furthermore, other feature points, such as comer 
points, can be detected as geometric primitives by using 
Harris or SUSAN operators. In addition to using a geometric 
constraint, other primitives can probably be used for the 
prioritization of the correspondences. And finally, the 
proposed algorithm offers a pair-wise registration scheme. It 
can be extended into a multi-scan registration. 
Table 3: Comparison of matching results with or without 
geometric constraint. 
Pair 
A coO 
A^SO 
> 
o 
AX(m) 
AY(m) 
AZ(m) 
01-02 
-0.031 
-0.001 
0.022 
-0.001 
-0.017 
0.010 
01-03 
0.051 
-0.082 
-0.078 
0.018 
0.021 
0.038 
01-03a 
-0.075 
-0.013 
-0.032 
0.032 
-0.048 
0.051 
01-04 
0.037 
0.079 
0.021 
-0.053 
-0.011 
-0.025 
01-05 
-0.028 
-0.104 
0.115 
-0.056 
0.051 
0.047 
01-05a 
-0.108 
0.126 
0.022 
0.091 
-0.039 
0.013 
01-06 
— 
— 
— 
— 
— 
— 
02-03 
-0.018 
0.011 
-0.009 
-0.031 
0.019 
-0.017 
03-03a 
0.033 
-0.009 
-0.026 
-0.027 
-0.021 
0.009 
03-04 
-0.031 
0.018 
-0.013 
0.017 
-0.018 
0.007 
04-05 
0.042 
-0.020 
-0.016 
-0.033 
0.032 
0.012 
05-05a 
-0.023 
0.019 
-0.021 
-0.025 
-0.016 
-0.014 
05-06 
0.029 
-0.014 
-0.011 
0.016 
-0.022 
0.019 
Table 4: Deviation of the translation and rotation parameters 
from the reference values for the registration based on 
proposed method 
ACKNOWLEDGEMENTS 
Claus Brenner has been funded by the VolkswagenStiftung, 
Germany. Zhi Wang has been funded by China Scholarship 
Council. 
REFERENCES 
Bae, K. and Lichti, D., 2004. Automated registration of 
unorganised point clouds from terrestrial laser scanners. 
International Archives of Photogrammetry and Remote 
Sensing 35 (Part B5), pp. 222-227. 
Bamea, S. and Filin, S., 2008. Keypoint based autonomous 
registration of terrestrial laser point-clouds. ISPRS Journal of 
Photogrammetry & Remote Sensing, Theme Issue: Terrestrial 
Laser Scanning 63, pp. 19-35. 
Bendels, G. H., Degener, P., Wahl, R., Kortgen, M. and Klein, 
R., 2004. Image-based registration of 3d-range data using