Indoor place classification by building cardinal-direction prototyping blocks on point clouds.

Making robots know people's place concepts has attracted researchers for decades. People believe that this capability will firmly benefit not only robot–human interaction but also reasonable and social robot behaviors, or even traditional problems in robot research such as object detection. Focusing on place classification, this paper builds a kind of native pure 3D geometric description to capture place layouts based on common point clouds. This perspective enables our method to naturally accommodate various illuminations, including extremely bad lighting for which traditional image methods cannot work properly. The space of a place is first divided into 3D voxels. The cardinal orientations of this space are then extracted, and the geometric attributes of the voxels are subsequently represented based on the cardinal orientations. The voxels with geometric attributes are defined as the cardinal-direction prototyping blocks (CDPBs). Next, the CDPB distribution for a scene is calculated by qualitative spatial description technology, thereby obtaining the complete place description. Given the sparse description, the sparse random forest (SRF) is used for learning. The experiments indicate that the CDPB-based method outperforms the current 3D geometric method and its mixed method, and it has good time performance. The main advantages of our method are that it does not require any strict hypotheses on surfaces, such as planar surfaces, it requires smaller fusion windows to attain satisfactory classification rates, it can be used in extreme lighting environments, and its parameter selection is easy. • A native place–layout representation method of an appropriate granularity. • A novel qualitative description of point clouds. • A pure 3D method that can be used in extreme lighting environments. • Outperforms 3D geometry mixed method and has better real-time property. • We attempt to discover the limitations of the pure 3D geometry method. [ABSTRACT FROM AUTHOR]