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**Harvard**

Eriksson, D. (2014) *Point Cloud Simplification and Processing for Path-Planning*. Göteborg : Chalmers University of Technology

** BibTeX **

@mastersthesis{

Eriksson2014,

author={Eriksson, David},

title={Point Cloud Simplification and Processing for Path-Planning},

abstract={Recently the area of motion planning research has been experiencing a significant resurgence
of interest based on hybrid working environments that combine point and CAD
models. Companies are able to work with point clouds and perform certain operations,
such as path-planning, but they lack the support for fast shortest-distance computations
for point clouds with more than tens of millions of points. Therefore, there is a need for
handling and pre-processing massive point clouds for fast-queries.
In this thesis, algorithms have been developed that are capable of efficiently preprocessing
massive point clouds for fast out-of-core queries allowing rapid computation
of the exact shortest distance between a point cloud and a triangulated object. This is
achieved by exploiting fast approximate distance computations between subsets of points
and the triangulated object.
This approach was able to compute, on average, the shortest distance in 15 fps for
a point cloud having 1 billion points, given only 8 GB of RAM. The findings and implementations
will have a direct impact for the many companies that want to perform
path-planning through massive point clouds since the algorithms are able to produce
near real-time distance computations on a standard PC.},

publisher={Institutionen för matematiska vetenskaper, matematik, Chalmers tekniska högskola},

place={Göteborg},

year={2014},

note={63},

}

** RefWorks **

RT Generic

SR Electronic

ID 196828

A1 Eriksson, David

T1 Point Cloud Simplification and Processing for Path-Planning

YR 2014

AB Recently the area of motion planning research has been experiencing a significant resurgence
of interest based on hybrid working environments that combine point and CAD
models. Companies are able to work with point clouds and perform certain operations,
such as path-planning, but they lack the support for fast shortest-distance computations
for point clouds with more than tens of millions of points. Therefore, there is a need for
handling and pre-processing massive point clouds for fast-queries.
In this thesis, algorithms have been developed that are capable of efficiently preprocessing
massive point clouds for fast out-of-core queries allowing rapid computation
of the exact shortest distance between a point cloud and a triangulated object. This is
achieved by exploiting fast approximate distance computations between subsets of points
and the triangulated object.
This approach was able to compute, on average, the shortest distance in 15 fps for
a point cloud having 1 billion points, given only 8 GB of RAM. The findings and implementations
will have a direct impact for the many companies that want to perform
path-planning through massive point clouds since the algorithms are able to produce
near real-time distance computations on a standard PC.

PB Institutionen för matematiska vetenskaper, matematik, Chalmers tekniska högskola,

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/196828/196828.pdf

OL 30