Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Geometric Surface based Segmentation of Point Cloud from Lidar Data   Mapping the environment has been an important task for robot navigation and
Simultaneous Localization And Mapping (SLAM). LIDAR provides a fast and
accurate 3D point cloud map of the environment which helps in map building.
However, processing millions of points in the point cloud becomes a
computationally expensive task. In this paper, a methodology is presented to
generate the segmented surfaces in real time and these can be used in modeling
the 3D objects. At first an algorithm is proposed for efficient map building
from single shot data of spinning Lidar. It is based on fast meshing and
sub-sampling. It exploits the physical design and the working principle of the
spinning Lidar sensor. The generated mesh surfaces are then segmented by
estimating the normal and considering their homogeneity. The segmented surfaces
can be used as proposals for predicting geometrically accurate model of objects
in the robots activity environment. The proposed methodology is compared with
some popular point cloud segmentation methods to highlight the efficacy in
terms of accuracy and speed.

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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: stat
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The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol