![](bg6.png)
(a) Unregistered 3D scans. (b) Registered 3D scans.
Figure 3: Registration of 3D scans with non-zero roll, pitch, and yaw angles.
by a 2D ICP algorithm with orthogonal virtual 2D
scans, utilizing the typical structure of indoor envi-
ronments. Due to the use of virtual 2D scans, the al-
gorithm can cope with dynamic objects (with small
restictions) as well as with non-rectangular environ-
ments. The resulting registration leads to a similar
quality to the full 3D ICP algorithm.
Limitations and therefore future research work
have been identified in sections 4.2 and 4.3. Never-
theless, our experiments showed promising results. A
comparison to other approaches like techniques based
on the extraction of planes out of the 3D range images
and matching them will be necessary to stress the ad-
vantages of our algorithm. Further future work will
deal with the implementation and evaluation of a full
SLAM cycle based on the registration of 3D range
images using virtual 2D scans.
REFERENCES
Arun, K. S., Huang, T. S., and Blostein, S. D. (1987). Least-
squares fitting of two 3-D point sets. IEEE Trans. Pat-
tern Anal. Mach. Intell., 9(5):698–700.
Bentley, J. L. (1975). Multidimensional binary search
trees used for associative searching. Commun. ACM,
18(9):509–517.
Besl, P. and McKay, H. (1992). A method for registration
of 3-D shapes. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 14(2):239–256.
Chen, Y. and Medioni, G. (1991). Object modeling by reg-
istration of multiple range images. In Proc. IEEE In-
ternational Conference on Robotics and Automation
ICRA 1991, pages 2724–2729 vol.3.
Jez, O. (2008). 3D mapping and localization using lev-
eled map accelerated ICP. In Bruyninckx, H., Preucil,
L., and Kulich, M., editors, EUROS, volume 44 of
Springer Tracts in Advanced Robotics, pages 343–
353. Springer.
Magnusson, M., N
¨
uchter, A., Lorken, C., Lilienthal, A., and
Hertzberg, J. (2009). Evaluation of 3d registration re-
liability and speed - a comparison of icp and ndt. In
Proc. IEEE International Conference on Robotics and
Automation ICRA 2009, pages 3907–3912.
Nguyen, V., Harati, A., and Siegwart, R. (2007). A
lightweight SLAM algorithm using orthogonal planes
for indoor mobile robotics. In Proc. IEEE/RSJ In-
ternational Conference on Intelligent Robots and Sys-
tems IROS 2007, pages 658–663.
N
¨
uchter, A., Lingemann, K., and Hertzberg, J. (2005).
Mapping of rescue environments with Kurt3D. In
Proc. IEEE International Safety, Security and Rescue
Robotics, Workshop, pages 158–163.
N
¨
uchter, A., Lingemann, K., Hertzberg, J., and Surmann,
H. (2007). 6D SLAM - 3D mapping outdoor environ-
ments. J. Field Robot., 24(8-9):699–722.
Thrun, S. (2003). Robotic mapping: a survey, pages 1–
35. Morgan Kaufmann Publishers Inc., San Francisco,
CA, USA.
Wulf, O. (2008). Virtuelle 2D-Scans - Ein Verfahren zur
echtzeitf
¨
ahigen Roboternavigation mit dreidimension-
alen Umgebungsdaten. PhD thesis, Leibniz Univer-
sit
¨
at Hannover, Hannover, Germany.
Wulf, O., Arras, K., Christensen, H., and Wagner, B.
(2004). 2D mapping of cluttered indoor environments
by means of 3D perception. In Proc. IEEE Interna-
tional Conference on Robotics and Automation ICRA
2004, volume 4, pages 4204–4209 Vol.4.
Wulf, O., N
¨
uchter, A., Hertzberg, J., and Wagner, B. (2007).
Ground truth evaluation of large urban 6D SLAM. In
Proc. IEEE/RSJ International Conference on Intelli-
gent Robots and Systems IROS 2007, pages 650–657.
ICINCO 2010 - 7th International Conference on Informatics in Control, Automation and Robotics
332