Experimental Comparison of Two 6D Pose Estimation Algorithms in Robotic Fruit-Picking Tasks
This paper presents an experimental comparison between two existing methods representative of two categories of 6D pose estimation algorithms nowadays commonly used in the robotics community. The first category includes purely deep learning methods, while the second one includes hybrid approaches co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-08-01
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| Series: | Robotics |
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| Online Access: | https://www.mdpi.com/2218-6581/13/9/127 |
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| author | Alessio Benito Alterani Marco Costanzo Marco De Simone Sara Federico Ciro Natale |
| author_facet | Alessio Benito Alterani Marco Costanzo Marco De Simone Sara Federico Ciro Natale |
| author_sort | Alessio Benito Alterani |
| collection | DOAJ |
| description | This paper presents an experimental comparison between two existing methods representative of two categories of 6D pose estimation algorithms nowadays commonly used in the robotics community. The first category includes purely deep learning methods, while the second one includes hybrid approaches combining learning pipelines and geometric reasoning. The hybrid method considered in this paper is a pipeline of an instance-level deep neural network based on RGB data only and a geometric pose refinement algorithm based on the availability of the depth map and the CAD model of the target object. Such a method can handle objects whose dimensions differ from those of the CAD. The pure learning method considered in this comparison is DenseFusion, a consolidated state-of-the-art pose estimation algorithm selected because it uses the same input data, namely, RGB image and depth map. The comparison is carried out by testing the success rate of fresh food pick-and-place operations. The fruit-picking scenario has been selected for the comparison because it is challenging due to the high variability of object instances in appearance and dimensions. The experiments carried out with apples and limes show that the hybrid method outperforms the pure learning one in terms of accuracy, thus allowing the pick-and-place operation of fruits with a higher success rate. An extensive discussion is also presented to help the robotics community select the category of 6D pose estimation algorithms most suitable to the specific application. |
| format | Article |
| id | doaj-art-5b36fc0ba5044130a75d8bc45adce3de |
| institution | OA Journals |
| issn | 2218-6581 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj-art-5b36fc0ba5044130a75d8bc45adce3de2025-08-20T01:55:49ZengMDPI AGRobotics2218-65812024-08-0113912710.3390/robotics13090127Experimental Comparison of Two 6D Pose Estimation Algorithms in Robotic Fruit-Picking TasksAlessio Benito Alterani0Marco Costanzo1Marco De Simone2Sara Federico3Ciro Natale4Dipartimento di Ingegneria, Università degli Studi della Campania Luigi Vanvitelli, Via Roma, 29, 81031 Aversa, CE, ItalyDipartimento di Ingegneria, Università degli Studi della Campania Luigi Vanvitelli, Via Roma, 29, 81031 Aversa, CE, ItalyDipartimento di Ingegneria, Università degli Studi della Campania Luigi Vanvitelli, Via Roma, 29, 81031 Aversa, CE, ItalyDipartimento di Ingegneria, Università degli Studi della Campania Luigi Vanvitelli, Via Roma, 29, 81031 Aversa, CE, ItalyDipartimento di Ingegneria, Università degli Studi della Campania Luigi Vanvitelli, Via Roma, 29, 81031 Aversa, CE, ItalyThis paper presents an experimental comparison between two existing methods representative of two categories of 6D pose estimation algorithms nowadays commonly used in the robotics community. The first category includes purely deep learning methods, while the second one includes hybrid approaches combining learning pipelines and geometric reasoning. The hybrid method considered in this paper is a pipeline of an instance-level deep neural network based on RGB data only and a geometric pose refinement algorithm based on the availability of the depth map and the CAD model of the target object. Such a method can handle objects whose dimensions differ from those of the CAD. The pure learning method considered in this comparison is DenseFusion, a consolidated state-of-the-art pose estimation algorithm selected because it uses the same input data, namely, RGB image and depth map. The comparison is carried out by testing the success rate of fresh food pick-and-place operations. The fruit-picking scenario has been selected for the comparison because it is challenging due to the high variability of object instances in appearance and dimensions. The experiments carried out with apples and limes show that the hybrid method outperforms the pure learning one in terms of accuracy, thus allowing the pick-and-place operation of fruits with a higher success rate. An extensive discussion is also presented to help the robotics community select the category of 6D pose estimation algorithms most suitable to the specific application.https://www.mdpi.com/2218-6581/13/9/127pose estimationrobotic graspingdeep learning |
| spellingShingle | Alessio Benito Alterani Marco Costanzo Marco De Simone Sara Federico Ciro Natale Experimental Comparison of Two 6D Pose Estimation Algorithms in Robotic Fruit-Picking Tasks Robotics pose estimation robotic grasping deep learning |
| title | Experimental Comparison of Two 6D Pose Estimation Algorithms in Robotic Fruit-Picking Tasks |
| title_full | Experimental Comparison of Two 6D Pose Estimation Algorithms in Robotic Fruit-Picking Tasks |
| title_fullStr | Experimental Comparison of Two 6D Pose Estimation Algorithms in Robotic Fruit-Picking Tasks |
| title_full_unstemmed | Experimental Comparison of Two 6D Pose Estimation Algorithms in Robotic Fruit-Picking Tasks |
| title_short | Experimental Comparison of Two 6D Pose Estimation Algorithms in Robotic Fruit-Picking Tasks |
| title_sort | experimental comparison of two 6d pose estimation algorithms in robotic fruit picking tasks |
| topic | pose estimation robotic grasping deep learning |
| url | https://www.mdpi.com/2218-6581/13/9/127 |
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