The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part Bl. Beijing 2008 
CONCLUSIONS 
This paper introduced a method to reconstruct the trajectory of 
a moving laser scanner on the basis of indoor scans made at 
different positions. The method is composed of two main parts: 
First, obtaining segmentations of successive scans, second, 
localizing the scanner with respect to the mapped scene that is 
composed of the extracted planes in the segmented range 
images. The segmentation is entirely carried out using fast 2D 
image processing operations, and can be executed in real-time. 
The localization is based on keeping track of at least three 
intersecting planes in successive scans, and measuring 
distances to these planes. The method was shown to yield a 
positioning accuracy in the order of a few centimeters within 7 
scans in an area of about 5 meters. The processing times also 
indicated computational efficiency of the method. 
For this approach to be useful, for example in autonomous 
robot navigation, fast, affordable, and light equipment would 
be required that can be easily handled. This is not fulfilled 
when using the kind of terrestrial laser scanners presented in 
the experiment (FARO 880). Also, the time these scanners 
need for a single scan at each position does not favor real-time 
navigation. Alternative devices, such as SwissRanger by 
MESA, that can make 3d scans at video rates, are increasingly 
available, and are already being proposed as robot navigation 
sensors by various authors. The point density of such scanners 
is much lower, as is the signal-to-noise ratio of the distance 
measurements. However, this would only influence the 
positioning accuracy, and should have a minor impact on the 
navigation of the laser scanner within the mapped scene. 
ACKNOWLEDGMENTS 
The research was executed within a project called RGI-150: 
Indoor positioning and navigation, sponsored by the Dutch 
Ministry of Economic Affairs within the BSIK program. 
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