Object recognition and 6D pose estimation using deep learning

Object recognition and 6D pose estimation are imperative for robots to relate to the real world. However, due to occlusion, clutter and the properties of various objects in a scene, it might be challenging and tedious for a robot to recognize and estimate the 6D pose of objects. Various methods have...

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Main Author: Sivadas Thinagar Nanoo
Other Authors: Teoh Eam Khwang
Format: Final Year Project
Language:English
Published: 2019
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Online Access:http://hdl.handle.net/10356/77466
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-774662023-07-07T16:44:44Z Object recognition and 6D pose estimation using deep learning Sivadas Thinagar Nanoo Teoh Eam Khwang Wang Kong Wah School of Electrical and Electronic Engineering A*STAR DRNTU::Engineering::Electrical and electronic engineering Object recognition and 6D pose estimation are imperative for robots to relate to the real world. However, due to occlusion, clutter and the properties of various objects in a scene, it might be challenging and tedious for a robot to recognize and estimate the 6D pose of objects. Various methods have been presented throughout the years with concern to this topic. However, many of these methods have its set back and limitations. Due to these reasons, rose the motivation to develop a robust and versatile real time system capable of accurate object recognition and 6D pose estimation with respect to the industrial standards. Over the years, with the advancement in technology, computing power have improved drastically. Algorithms, techniques and methods that were once infeasible to implement due to high computational power requirements could now be done with ease. One such implementation is none other than deep learning. It is now the current state-of-the-art technology. Since, this project is in relation with computer vision, the deep learning architecture proposed would be a convolutional neural network. Hence, the dataset used would consist of images. A deep learning framework known as PoseCNN is explored for object recognition and 6D pose estimation capabilities. In this project, a detailed literature review of PoseCNN, as well as comparisons with current approaches, will be reviewed. Finally, the results obtained using the YCB dataset would be presented. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-05-29T06:55:51Z 2019-05-29T06:55:51Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77466 en Nanyang Technological University 134 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Sivadas Thinagar Nanoo
Object recognition and 6D pose estimation using deep learning
description Object recognition and 6D pose estimation are imperative for robots to relate to the real world. However, due to occlusion, clutter and the properties of various objects in a scene, it might be challenging and tedious for a robot to recognize and estimate the 6D pose of objects. Various methods have been presented throughout the years with concern to this topic. However, many of these methods have its set back and limitations. Due to these reasons, rose the motivation to develop a robust and versatile real time system capable of accurate object recognition and 6D pose estimation with respect to the industrial standards. Over the years, with the advancement in technology, computing power have improved drastically. Algorithms, techniques and methods that were once infeasible to implement due to high computational power requirements could now be done with ease. One such implementation is none other than deep learning. It is now the current state-of-the-art technology. Since, this project is in relation with computer vision, the deep learning architecture proposed would be a convolutional neural network. Hence, the dataset used would consist of images. A deep learning framework known as PoseCNN is explored for object recognition and 6D pose estimation capabilities. In this project, a detailed literature review of PoseCNN, as well as comparisons with current approaches, will be reviewed. Finally, the results obtained using the YCB dataset would be presented.
author2 Teoh Eam Khwang
author_facet Teoh Eam Khwang
Sivadas Thinagar Nanoo
format Final Year Project
author Sivadas Thinagar Nanoo
author_sort Sivadas Thinagar Nanoo
title Object recognition and 6D pose estimation using deep learning
title_short Object recognition and 6D pose estimation using deep learning
title_full Object recognition and 6D pose estimation using deep learning
title_fullStr Object recognition and 6D pose estimation using deep learning
title_full_unstemmed Object recognition and 6D pose estimation using deep learning
title_sort object recognition and 6d pose estimation using deep learning
publishDate 2019
url http://hdl.handle.net/10356/77466
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