Funk et al 2016a - Revision history

Scipediacontent at 14:06, 14 October 2020

2020-10-14T14:06:22Z

Scipediacontent at 14:05, 14 October 2020

2020-10-14T14:05:35Z

Scipediacontent: Scipediacontent moved page Draft Content 499227026 to Funk et al 2016a

2020-10-14T14:01:43Z

Scipediacontent moved page Draft Content 499227026 to Funk et al 2016a

Scipediacontent: Created page with " == Abstract == The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable c..."

2020-10-14T14:01:40Z

Created page with " == Abstract == The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable c..."

New page

== Abstract ==

The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within.\ud \ud This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy.

Document type: Part of book or chapter of book

== Original document ==

The different versions of the original document can be found in:

* [http://oro.open.ac.uk/45399/1/Springer_Eugen_Paper_Feb%202016.pdf http://oro.open.ac.uk/45399/1/Springer_Eugen_Paper_Feb%202016.pdf]

@@ Line 7: / Line 7: @@
 == Original document ==
 The different versions of the original document can be found in:
 * [http://oro.open.ac.uk/45399/1/Springer_Eugen_Paper_Feb%202016.pdf http://oro.open.ac.uk/45399/1/Springer_Eugen_Paper_Feb%202016.pdf]

@@ Line 2: / Line 2: @@
 == Abstract ==
-The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within.\ud \ud This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy.
+The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within. This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy.
 Document type: Part of book or chapter of book
 == Original document ==

← Older revision	Revision as of 14:01, 14 October 2020
(No difference)