A Novel Distribution for Representation of 6D Pose Uncertainty
The 6D Pose estimation is a crux in many applications, such as visual perception, autonomous navigation, and spacecraft motion. For robotic grasping, the cluttered and self-occlusion scenarios bring new challenges to the this field. Currently, society uses CNNs to solve this problem. The CNN models...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/13/1/126 |
| _version_ | 1797491847280459776 |
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| author | Lei Zhang Huiliang Shang Yandan Lin |
| author_facet | Lei Zhang Huiliang Shang Yandan Lin |
| author_sort | Lei Zhang |
| collection | DOAJ |
| description | The 6D Pose estimation is a crux in many applications, such as visual perception, autonomous navigation, and spacecraft motion. For robotic grasping, the cluttered and self-occlusion scenarios bring new challenges to the this field. Currently, society uses CNNs to solve this problem. The CNN models will suffer high uncertainty caused by the environmental factors and the object itself. These models usually maintain a Gaussian distribution, which is not suitable for the underlying manifold structure of the pose. Many works decouple rotation from the translation and quantify rotational uncertainty. Only a few works pay attention to the uncertainty of the 6D pose. This work proposes a distribution that can capture the uncertainty of the 6D pose parameterized by the dual quaternions, meanwhile, the proposed distribution takes the periodic nature of the underlying structure into account. The presented results include the normalization constant computation and parameter estimation techniques of the distribution. This work shows the benefits of the proposed distribution, which provides a more realistic explanation for the uncertainty in the 6D pose and eliminates the drawback inherited from the planar rigid motion. |
| first_indexed | 2024-03-10T00:55:08Z |
| format | Article |
| id | doaj.art-ca75a53d67fd4ee3be9ee8cb48c33d86 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T00:55:08Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-ca75a53d67fd4ee3be9ee8cb48c33d862023-11-23T14:45:22ZengMDPI AGMicromachines2072-666X2022-01-0113112610.3390/mi13010126A Novel Distribution for Representation of 6D Pose UncertaintyLei Zhang0Huiliang Shang1Yandan Lin2Academy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaSchool of Information Science and Technology, Fudan University, Shanghai 200433, ChinaInstitute for Electric Light Sources, School of Information Science and Technology, Fudan University, Shanghai 200433, ChinaThe 6D Pose estimation is a crux in many applications, such as visual perception, autonomous navigation, and spacecraft motion. For robotic grasping, the cluttered and self-occlusion scenarios bring new challenges to the this field. Currently, society uses CNNs to solve this problem. The CNN models will suffer high uncertainty caused by the environmental factors and the object itself. These models usually maintain a Gaussian distribution, which is not suitable for the underlying manifold structure of the pose. Many works decouple rotation from the translation and quantify rotational uncertainty. Only a few works pay attention to the uncertainty of the 6D pose. This work proposes a distribution that can capture the uncertainty of the 6D pose parameterized by the dual quaternions, meanwhile, the proposed distribution takes the periodic nature of the underlying structure into account. The presented results include the normalization constant computation and parameter estimation techniques of the distribution. This work shows the benefits of the proposed distribution, which provides a more realistic explanation for the uncertainty in the 6D pose and eliminates the drawback inherited from the planar rigid motion.https://www.mdpi.com/2072-666X/13/1/126probability theorydual quaternionpose uncertaintylie groupBingham distribution |
| spellingShingle | Lei Zhang Huiliang Shang Yandan Lin A Novel Distribution for Representation of 6D Pose Uncertainty Micromachines probability theory dual quaternion pose uncertainty lie group Bingham distribution |
| title | A Novel Distribution for Representation of 6D Pose Uncertainty |
| title_full | A Novel Distribution for Representation of 6D Pose Uncertainty |
| title_fullStr | A Novel Distribution for Representation of 6D Pose Uncertainty |
| title_full_unstemmed | A Novel Distribution for Representation of 6D Pose Uncertainty |
| title_short | A Novel Distribution for Representation of 6D Pose Uncertainty |
| title_sort | novel distribution for representation of 6d pose uncertainty |
| topic | probability theory dual quaternion pose uncertainty lie group Bingham distribution |
| url | https://www.mdpi.com/2072-666X/13/1/126 |
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