Image-based deep learning algorithm for coarse multi-robot localization
Multi-robot systems are of great importance for tasks that require more robust and faster operation. It can be applied to different scenarios such as search and rescue, modern agriculture, self-driving vehicles and more. The principle work is accurately locating each robot’s position in the global c...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/78098 |
| _version_ | 1826115571721175040 |
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| author | Ye, Yingjian |
| author2 | Wang Dan Wei |
| author_facet | Wang Dan Wei Ye, Yingjian |
| author_sort | Ye, Yingjian |
| collection | NTU |
| description | Multi-robot systems are of great importance for tasks that require more robust and faster operation. It can be applied to different scenarios such as search and rescue, modern agriculture, self-driving vehicles and more. The principle work is accurately locating each robot’s position in the global coordinate frame, so that the map generated by each robot can be continuously fused together. In this paper, an approach on helping the principal researchers for collecting and pre-processing image data has been done. Furthermore, the author uses a convolutional neural network (CNN) architecture to train the model and correctly recognize locations under dynamic environment. |
| first_indexed | 2024-10-01T03:57:21Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/78098 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:57:21Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/780982023-07-07T17:02:24Z Image-based deep learning algorithm for coarse multi-robot localization Ye, Yingjian Wang Dan Wei School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Multi-robot systems are of great importance for tasks that require more robust and faster operation. It can be applied to different scenarios such as search and rescue, modern agriculture, self-driving vehicles and more. The principle work is accurately locating each robot’s position in the global coordinate frame, so that the map generated by each robot can be continuously fused together. In this paper, an approach on helping the principal researchers for collecting and pre-processing image data has been done. Furthermore, the author uses a convolutional neural network (CNN) architecture to train the model and correctly recognize locations under dynamic environment. Bachelor of Engineering (Computer Engineering) 2019-06-12T02:49:55Z 2019-06-12T02:49:55Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78098 en Nanyang Technological University 48 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Ye, Yingjian Image-based deep learning algorithm for coarse multi-robot localization |
| title | Image-based deep learning algorithm for coarse multi-robot localization |
| title_full | Image-based deep learning algorithm for coarse multi-robot localization |
| title_fullStr | Image-based deep learning algorithm for coarse multi-robot localization |
| title_full_unstemmed | Image-based deep learning algorithm for coarse multi-robot localization |
| title_short | Image-based deep learning algorithm for coarse multi-robot localization |
| title_sort | image based deep learning algorithm for coarse multi robot localization |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics |
| url | http://hdl.handle.net/10356/78098 |
| work_keys_str_mv | AT yeyingjian imagebaseddeeplearningalgorithmforcoarsemultirobotlocalization |