6DOF Object Recognition and Positioning for Robotics Using Next Best View Heuristics
(2018) In LU-CS-EX 2018-12 EDAM05 20172Department 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:
http://lup.lub.lu.se/student-papers/record/8936660
- 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
- 2018
- 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}}, }