Home Summary Discussion Support Code Documentation Publications Wishlist Data Sets		A tutorial on how to perform a global optimization on a sequentially matched data set In this tutorial we are going to use the hannover data set for illustration. You can download the hannover data set from
slam6D -s 1 -e 65 -r 10 -i 100 -d 75 --epsICP=0.00001 -D 250 -I 50 --cldist=750 -G 1 /home/dat/hannover1

It can be seen that the command line for simultaneous matching contains all the parameters for sequential matching and some additional parameters. Using these parameters first sequential scan matching is performed as explained in the tutorial on sequential matching. Afterwards the GraphSLAM approach described in RAS and

-D 250

This parameter specifies that the maximum point-to-point distance for matching should be 250.

-I 50

This parameter specifies that the maximum number of iterations should be 50.

--cldist=750

This parameter determines which scans are allowed to match against each other. In this case if the distance between two points is more than 750, they will not be matched against each other.

-G 1

This parameter selects the algorithm for simultaneous matching. The default is set to 0 in which case no matching is done.

/home/dat/hannover1/

This is the destination path for the folder containing the scans to be registered. This may vary depending on where you save the hannover1 data set.

To visualise or animate the data set you can refer to the show tutorials on this page. An example command for using show to graphically display the simultaneously matched data set is:

show -s 1 -e 65

This will display the simultaneously matched data set starting from the 1st scan and ending on the 65th scan.

Below are shots of the output generated by show for the simultaneously matched data set.





It can be seen from the second shot that the overlapping of the wall error in the sequentially matched data set is removed in the global optimization.

Related Publications

• Dorit Borrmann, Jan Elseberg, Kai Lingemann, Andreas Nüchter, and Joachim Hertzberg. Globally consistent 3D mapping with scan matching. Journal of Robotics and Autonomous Systems (JRAS), Elsevier Science, Volume 56, Issue 2, ISSN 0921-8890, pages 130 - 142, February 2008, [ScienceDirect Link] [Get Paper] [Get Videos] [Addendum].

• Andreas Nüchter, Jan Elseberg, Peter Schneider, and Dietrich Paulus. Study of Parameterizations for the Rigid Body Transformations of The Scan Registration Problem, Journal Computer Vision and Image Understanding (CVIU), Elsevier Science, Volume 114, Issue 8, pp. 963-980, ISSN 1077-3142, August 2010. [Get Paper (PDF)] [Elsevier Link with supplementary content].

• Jochen Sprickerhof, Andreas Nüchter, Kai Lingemann, Joachim Hertzberg. An Explicit Loop Closing Technique for 6D SLAM, In Proceedings of the 4th European Conference on Mobile Robots (ECMR '09), Mlini/Dubrovnic, Croatia, September 2009. [Get Paper (PDF)] [Get Videos].

• Dorit Borrmann, Jan Elseberg, Kai Lingemann, Andreas Nüchter, and Joachim Hertzberg. The Efficient Extension of Globally Consistent Scan Matching to 6 DoF. In Proceedings of the 4th International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT '08), June 2008. [Get Paper] [Get Video] [Addendum].