  In the field of SLAM (Simultaneous Localization And Mapping) for robot
navigation, mapping the environment is an important task. In this regard the
Lidar sensor can produce near accurate 3D map of the environment in the format
of point cloud, in real time. Though the data is adequate for extracting
information related to SLAM, processing millions of points in the point cloud
is computationally quite expensive. The methodology presented proposes a fast
algorithm that can be used to extract semantically labelled surface segments
from the cloud, in real time, for direct navigational use or higher level
contextual scene reconstruction. First, a single scan from a spinning Lidar is
used to generate a mesh of subsampled cloud points online. The generated mesh
is further used for surface normal computation of those points on the basis of
which surface segments are estimated. A novel descriptor to represent the
surface segments is proposed and utilized to determine the surface class of the
segments (semantic label) with the help of classifier. These semantic surface
segments can be further utilized for geometric reconstruction of objects in the
scene, or can be used for optimized trajectory planning by a robot. The
proposed methodology is compared with number of point cloud segmentation
methods and state of the art semantic segmentation methods to emphasize its
efficacy in terms of speed and accuracy.
