Some groups have attempted to build 3D volumetric representations of environments with 2D laser range finders. Thrun et al. [23] 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. 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.
The published 2D probabilistic localization approaches, e.g., Markov models or Monte Carlo algorithms work well in flat and structured 2D environments but an extension in the third dimension is still missing since the algorithm do not scale with additional dimensions. Common approaches to SLAM use global relaxation after incremental 2D scan matching in order to create a globally consistent map [11].
A few other groups use 3D laser scanners [1,14,22]. A 3D laser scanner generates consistent 3D data points within a single 3D scan. The AVENUE project develops a robot for modeling urban environments using a CYRAX laser scanner and a feature-based scan matching approach for registration of the 3D scans in a common coordinate system [1]. The research group of M. Hebert has reconstructed environments using the Zoller+Fröhlich laser scanner and aims at building 3D models without initial position estimates, i.e., without odometry information [14].