Ma, Lingni and Whelan, Thomas and Bondarev, Egor and de With, Peter H.N. and McDonald, John
(2013)
Planar Simplification and Texturing of Dense Point Cloud Maps.
In:
European Conference on Mobile Robots (ECMR), 2013.
IEEE, pp. 164-171.
Abstract
Dense RGB-D based SLAM techniques and highfidelity
LIDAR scanners are examples from an abundant set of
systems capable of providing multi-million point datasets. These
large datasets quickly become difficult to process and work
with due to the sheer volume of data, which typically contains
significant redundant information, such as the representation
of planar surfaces with hundreds of thousands of points. In
order to exploit the richness of information provided by dense
methods in real-time robotics, techniques are required to reduce
the inherent redundancy of the data. In this paper we present
a method for efficient triangulation and texturing of planar
surfaces in large point clouds. Experimental results show that
our algorithm removes more than 90% of the input planar
points, leading to a triangulation with only 10% of the original
amount of triangles per planar segment, improving upon
an existing planar simplification algorithm. Despite the large
reduction in vertex count, the principal geometric features of
each segment are well preserved. In addition to this, our texture
generation algorithm preserves all colour information contained
within planar segments, resulting in a visually appealing and
geometrically accurate simplified representation.
Repository Staff Only(login required)
 |
Item control page |
Downloads per month over past year
Altmetric
Altmetric