Some groups have attempted to build 3D volumetric representations
of environments with 2D laser range finders. Thrun et al.
[#!Thrun_2000!#], Früh et al. [#!Frueh_2001_1!#] and Zhao et
al. [#!Zhao_2001!#] use two 2D laser range finder for acquiring
3D data. One laser scanner is mounted horizontally and one is
mounted vertically. The latter one grabs a vertical scan line
which is transformed into 3D points using the current robot
pose. Since the vertical scanner is not able to scan sides of
objects, Zhao et al. [#!Zhao_2001!#] use two additional vertical
mounted 2D scanner shifted by 
to reduce occlusion. The
horizontal scanner is used to compute the robot pose. The
precision of 3D data points depends on that pose and on the
precision of the scanner. All these approaches have difficulties
to navigate around 3D obstacles with jutting out edges. They are
only detected while passing them.
A few other groups use 3D laser scanners [#!Sequeira_1999!#,#!Allen_2001!#]. A 3D laser scanner generates consistent 3D data points within a single 3D scan. The RESOLV project aimed at modeling interiors for virtual reality and telepresence [#!Sequeira_1999!#]. They used a RIEGL laser range finder on two robots, called EST and AEST (Autonomous Environmental Sensor for Telepresence). They use the Iterative Closest Points (ICP) algorithm [#!Besl_1992!#] for scan matching and a perception planning module for minimizing occlusions. The AVENUE project develops a robot for modeling urban environments [#!Allen_2001!#] using a CYRAX laser scanner. They match 3D scans with camera images semi automatically to yield a textured model.
Other techniques for acquiring range data are stereo vision and photogrammetry. Stereo vision has difficulties with producing dense depth maps and depends on the illumination to some degree. Photogrammetric methods produce high quality models that are textured. Nevertheless, these methods are usually manual and computationally expensive, thus cannot be computed in real time or on a mobile system, i.e., on a robot. Tab. 1 compares laser scanning with photogrammetry. State of the art photogrammetric methods [#!Dias_2003!#] are combined with laser scanning to yield a measuring methodology that is more flexible. Dias et al. uses this combination to extract texture and to refine the model based on 3D laser scanning by images [#!Dias_2003!#]. It is possible to complete the models in areas where data is missing or to increase the resolution in areas of high interest and 3D contents.