Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Buch, Anders Glent"'
Publikováno v:
The 33rd British Machine Vision Conference Proceedings: BMVC 2022
The estimation of 6D poses of rigid objects is a fundamental problem in computer vision. Traditionally pose estimation is concerned with the determination of a single best estimate. However, a single estimate is unable to express visual ambiguity, wh
Externí odkaz:
http://arxiv.org/abs/2209.09659
Many industrial assembly tasks involve peg-in-hole like insertions with sub-millimeter tolerances which are challenging, even in highly calibrated robot cells. Visual servoing can be employed to increase the robustness towards uncertainties in the sy
Externí odkaz:
http://arxiv.org/abs/2206.08800
Autor:
Haugaard, Rasmus Laurvig, Iversen, Thorbjørn Mosekjær, Buch, Anders Glent, Kramberger, Aljaz, Mathiesen, Simon Faarvang
Fast, robust, and flexible part feeding is essential for enabling automation of low volume, high variance assembly tasks. An actuated vision-based solution on a traditional vibratory feeder, referred to here as a vision trap, should in principle be a
Externí odkaz:
http://arxiv.org/abs/2206.00373
Pose estimation is the task of determining the 6D position of an object in a scene. Pose estimation aid the abilities and flexibility of robotic set-ups. However, the system must be configured towards the use case to perform adequately. This configur
Externí odkaz:
http://arxiv.org/abs/2203.00945
We present an approach to learn dense, continuous 2D-3D correspondence distributions over the surface of objects from data with no prior knowledge of visual ambiguities like symmetry. We also present a new method for 6D pose estimation of rigid objec
Externí odkaz:
http://arxiv.org/abs/2111.13489
Since the introduction of modern deep learning methods for object pose estimation, test accuracy and efficiency has increased significantly. For training, however, large amounts of annotated training data are required for good performance. While the
Externí odkaz:
http://arxiv.org/abs/2011.08517
This paper demonstrates a visual servoing method which is robust towards uncertainties related to system calibration and grasping, while significantly reducing the peg-in-hole time compared to classical methods and recent attempts based on deep learn
Externí odkaz:
http://arxiv.org/abs/2011.06399
We present a learning-based method for 6 DoF pose estimation of rigid objects in point cloud data. Many recent learning-based approaches use primarily RGB information for detecting objects, in some cases with an added refinement step using depth data
Externí odkaz:
http://arxiv.org/abs/1912.09057
Autor:
Hodan, Tomas, Michel, Frank, Brachmann, Eric, Kehl, Wadim, Buch, Anders Glent, Kraft, Dirk, Drost, Bertram, Vidal, Joel, Ihrke, Stephan, Zabulis, Xenophon, Sahin, Caner, Manhardt, Fabian, Tombari, Federico, Kim, Tae-Kyun, Matas, Jiri, Rother, Carsten
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets i
Externí odkaz:
http://arxiv.org/abs/1808.08319
Publikováno v:
2017 IEEE International Conference on Computer Vision (ICCV)
It is possible to associate a highly constrained subset of relative 6 DoF poses between two 3D shapes, as long as the local surface orientation, the normal vector, is available at every surface point. Local shape features can be used to find putative
Externí odkaz:
http://arxiv.org/abs/1709.02142