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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 EDA920 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 in the... (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)
Popular Abstract (Swedish)
Robotar som anpassar sig efter omgivningen utan hjälp från människor blir vanligare och vanligare. Detta arbete skapar ett system för seende anpassat för robotar, som möjliggör identifiering och lokalisering av objekt.
Please use this url to cite or link to this publication:
author
Svensson, Andreas LU and Ganslandt, Alexander LU
supervisor
organization
course
EDA920 20172
year
type
H3 - Professional qualifications (4 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
8959961
date added to LUP
2018-10-15 10:09:29
date last changed
2018-10-15 10:09:29
@misc{8959961,
  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}},
}