  Mobile robots depend on maps for localization, planning, and other
applications. In indoor scenarios, there is often lots of clutter present, such
as chairs, tables, other furniture, or plants. While mapping this clutter is
important for certain applications, for example navigation, maps that represent
just the immobile parts of the environment, i.e. walls, are needed for other
applications, like room segmentation or long-term localization. In literature,
approaches can be found that use a complete point cloud to remove the furniture
in the room and generate a furniture free map. In contrast, we propose a
Simultaneous Localization And Mapping (SLAM)-based mobile laser scanning
solution. The robot uses an orthogonal pair of Lidars. The horizontal scanner
aims to estimate the robot position, whereas the vertical scanner generates the
furniture free map. There are three steps in our method: point cloud
rearrangement, wall plane detection and semantic labeling. In the experiment,
we evaluate the efficiency of removing furniture in a typical indoor
environment. We get $99.60\%$ precision in keeping the wall in the 3D result,
which shows that our algorithm can remove most of the furniture in the
environment. Furthermore, we introduce the application of 2D furniture free
mapping for room segmentation.
