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- Publisher Website: 10.3390/s18082719
- Scopus: eid_2-s2.0-85052058158
- PMID: 30126220
- WOS: WOS:000445712400319
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Article: Point pair feature-based pose estimation with multiple edge appearance models (PPF-MEAM) for robotic bin picking
Title | Point pair feature-based pose estimation with multiple edge appearance models (PPF-MEAM) for robotic bin picking |
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Authors | |
Keywords | Robotic bin picking Boundary-to-Boundary-using-Tangent-Line (B2B-TL) Pose estimation Multiple Edge AppearanceModels (MEAM) |
Issue Date | 2018 |
Citation | Sensors, 2018, v. 18, n. 8, article no. 2719 How to Cite? |
Abstract | Automation of the bin picking task with robots entails the key step of pose estimation, which identifies and locates objects so that the robot can pick and manipulate the object in an accurate and reliable way. This paper proposes a novel point pair feature-based descriptor named Boundary-to-Boundary-using-Tangent-Line (B2B-TL) to estimate the pose of industrial parts including some parts whose point clouds lack key details, for example, the point cloud of the ridges of a part. The proposed descriptor utilizes the 3D point cloud data and 2D image data of the scene simultaneously, and the 2D image data could compensate the missing key details of the point cloud. Based on the descriptor B2B-TL, Multiple Edge Appearance Models (MEAM), a method using multiple models to describe the target object, is proposed to increase the recognition rate and reduce the computation time. A novel pipeline of an online computation process is presented to take advantage of B2B-TL and MEAM. Our algorithm is evaluated against synthetic and real scenes and implemented in a bin picking system. The experimental results show that our method is sufficiently accurate for a robot to grasp industrial parts and is fast enough to be used in a real factory environment. |
Persistent Identifier | http://hdl.handle.net/10722/302998 |
ISSN | 2023 Impact Factor: 3.4 2023 SCImago Journal Rankings: 0.786 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liu, Diyi | - |
dc.contributor.author | Arai, Shogo | - |
dc.contributor.author | Miao, Jiaqi | - |
dc.contributor.author | Kinugawa, Jun | - |
dc.contributor.author | Wang, Zhao | - |
dc.contributor.author | Kosuge, Kazuhiro | - |
dc.date.accessioned | 2021-09-07T08:43:00Z | - |
dc.date.available | 2021-09-07T08:43:00Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Sensors, 2018, v. 18, n. 8, article no. 2719 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | http://hdl.handle.net/10722/302998 | - |
dc.description.abstract | Automation of the bin picking task with robots entails the key step of pose estimation, which identifies and locates objects so that the robot can pick and manipulate the object in an accurate and reliable way. This paper proposes a novel point pair feature-based descriptor named Boundary-to-Boundary-using-Tangent-Line (B2B-TL) to estimate the pose of industrial parts including some parts whose point clouds lack key details, for example, the point cloud of the ridges of a part. The proposed descriptor utilizes the 3D point cloud data and 2D image data of the scene simultaneously, and the 2D image data could compensate the missing key details of the point cloud. Based on the descriptor B2B-TL, Multiple Edge Appearance Models (MEAM), a method using multiple models to describe the target object, is proposed to increase the recognition rate and reduce the computation time. A novel pipeline of an online computation process is presented to take advantage of B2B-TL and MEAM. Our algorithm is evaluated against synthetic and real scenes and implemented in a bin picking system. The experimental results show that our method is sufficiently accurate for a robot to grasp industrial parts and is fast enough to be used in a real factory environment. | - |
dc.language | eng | - |
dc.relation.ispartof | Sensors | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Robotic bin picking | - |
dc.subject | Boundary-to-Boundary-using-Tangent-Line (B2B-TL) | - |
dc.subject | Pose estimation | - |
dc.subject | Multiple Edge AppearanceModels (MEAM) | - |
dc.title | Point pair feature-based pose estimation with multiple edge appearance models (PPF-MEAM) for robotic bin picking | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3390/s18082719 | - |
dc.identifier.pmid | 30126220 | - |
dc.identifier.pmcid | PMC6111311 | - |
dc.identifier.scopus | eid_2-s2.0-85052058158 | - |
dc.identifier.volume | 18 | - |
dc.identifier.issue | 8 | - |
dc.identifier.spage | article no. 2719 | - |
dc.identifier.epage | article no. 2719 | - |
dc.identifier.isi | WOS:000445712400319 | - |