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6DOF Object Recognition and Positioning for Robotics Using Next Best View Heuristics

Svensson, Andreas LU and Ganslandt, Alexander LU (2018) In LU-CS-EX 2018-12 EDAM05 20172
Department of Computer Science
Abstract
The accuracy and portability of depth cameras have increased by a lot in recent
years, which allows for advanced 3D scanning of the environment for robotic
applications. In this thesis we have developed a system that uses a depth camera mounted on a robot arm to identify and localize arbitrary objects, and give
hints on how to move the camera to get better localization results. The system
works by generating virtual views of objects to identify them in a point cloud
generated by the depth camera. This data is then used to estimate a pose of
the object, and generate a hint on where to move the camera next. After a new
point cloud is taken, it is merged with the previous cloud which allows the
system to iteratively get more confident... (More)
The accuracy and portability of depth cameras have increased by a lot in recent
years, which allows for advanced 3D scanning of the environment for robotic
applications. In this thesis we have developed a system that uses a depth camera mounted on a robot arm to identify and localize arbitrary objects, and give
hints on how to move the camera to get better localization results. The system
works by generating virtual views of objects to identify them in a point cloud
generated by the depth camera. This data is then used to estimate a pose of
the object, and generate a hint on where to move the camera next. After a new
point cloud is taken, it is merged with the previous cloud which allows the
system to iteratively get more confident in the identification and pose of the
object. (Less)
Please use this url to cite or link to this publication:
author
Svensson, Andreas LU and Ganslandt, Alexander LU
supervisor
organization
alternative title
6DOF Objektigenkänning och positionering för robotik med hjälp av Next Best View-heurestik
course
EDAM05 20172
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Robotics, point clouds, object identification, pose estimation, localization, point cloud merging, hint system, next best view
publication/series
LU-CS-EX 2018-12
report number
LU-CS-EX 2018-12
ISSN
1650-2884
language
English
id
8936660
alternative location
https://github.com/Laxen/object_identification_localization/blob/master/docs/report.pdf
date added to LUP
2018-12-19 13:37:57
date last changed
2018-12-19 13:37:57
@misc{8936660,
  abstract     = {{The accuracy and portability of depth cameras have increased by a lot in recent
years, which allows for advanced 3D scanning of the environment for robotic
applications. In this thesis we have developed a system that uses a depth camera mounted on a robot arm to identify and localize arbitrary objects, and give
hints on how to move the camera to get better localization results. The system
works by generating virtual views of objects to identify them in a point cloud
generated by the depth camera. This data is then used to estimate a pose of
the object, and generate a hint on where to move the camera next. After a new
point cloud is taken, it is merged with the previous cloud which allows the
system to iteratively get more confident in the identification and pose of the
object.}},
  author       = {{Svensson, Andreas and Ganslandt, Alexander}},
  issn         = {{1650-2884}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{LU-CS-EX 2018-12}},
  title        = {{6DOF Object Recognition and Positioning for Robotics Using Next Best View Heuristics}},
  year         = {{2018}},
}