6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping
| Autor: | Joel Vidal, Tuan-Tang Le, Yu-Ru Chen, Chyi-Yeu Lin, Trung-Son Le |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science General Mathematics Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Computer Science - Robotics 03 medical and health sciences 020901 industrial engineering & automation 0302 clinical medicine Computer vision Pose Artificial neural network business.industry Deep learning Cognitive neuroscience of visual object recognition Object (computer science) Computer Science Applications Task (computing) Control and Systems Engineering Feature (computer vision) 030220 oncology & carcinogenesis Metric (mathematics) Artificial intelligence business Robotics (cs.RO) Software |
| DOI: | 10.48550/arxiv.2111.06276 |
| Popis: | Real-time robotic grasping, supporting a subsequent precise object-in-hand operation task, is a priority target towards highly advanced autonomous systems. However, such an algorithm which can perform sufficiently-accurate grasping with time efficiency is yet to be found. This paper proposes a novel method with a 2-stage approach that combines a fast 2D object recognition using a deep neural network and a subsequent accurate and fast 6D pose estimation based on Point Pair Feature framework to form a real-time 3D object recognition and grasping solution capable of multi-object class scenes. The proposed solution has a potential to perform robustly on real-time applications, requiring both efficiency and accuracy. In order to validate our method, we conducted extensive and thorough experiments involving laborious preparation of our own dataset. The experiment results show that the proposed method scores 97.37% accuracy in 5cm5deg metric and 99.37% in Average Distance metric. Experiment results have shown an overall 62% relative improvement (5cm5deg metric) and 52.48% (Average Distance metric) by using the proposed method. Moreover, the pose estimation execution also showed an average improvement of 47.6% in running time. Finally, to illustrate the overall efficiency of the system in real-time operations, a pick-and-place robotic experiment is conducted and has shown a convincing success rate with 90% of accuracy. This experiment video is available at https://sites.google.com/view/dl-ppf6dpose/ . |
| Databáze: | OpenAIRE |
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