Hybrid SLAM and object recognition on an embedded platform
Simultaneous Localization and Mapping (SLAM) is a key component of modern autonomous robots. It provides a similar visualization and localization capability, that is easily perceived by a human, to an autonomous robot for it to function in an unfamiliar environment. However, a traditional SLAM syste...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157236 |
| _version_ | 1826129246262657024 |
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| author | Syahir Toriman |
| author2 | Lam Siew Kei |
| author_facet | Lam Siew Kei Syahir Toriman |
| author_sort | Syahir Toriman |
| collection | NTU |
| description | Simultaneous Localization and Mapping (SLAM) is a key component of modern autonomous robots. It provides a similar visualization and localization capability, that is easily perceived by a human, to an autonomous robot for it to function in an unfamiliar environment. However, a traditional SLAM system only creates a map that has no descriptive points of interest that may be useful for improved localization. In this project, a SLAM system is combined with a Text Detection and Recognition algorithm to provide a more descriptive visualization of the world. This composite system is designed and tested on the Jetson Xavier NX embedded platform. The ORB SLAM 2 algorithm was chosen for the SLAM system for its robustness and versatility. Then, the Efficient and Accurate Scene Text Detector (EAST) algorithm coupled with a Convolutional Recurrent Neural Network (CRNN) Scene Text Recognition was used to provide an efficient natural scene text detection and recognition. |
| first_indexed | 2024-10-01T07:37:30Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157236 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:37:30Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1572362022-05-12T23:41:36Z Hybrid SLAM and object recognition on an embedded platform Syahir Toriman Lam Siew Kei School of Computer Science and Engineering ASSKLam@ntu.edu.sg Engineering::Computer science and engineering::Hardware Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Simultaneous Localization and Mapping (SLAM) is a key component of modern autonomous robots. It provides a similar visualization and localization capability, that is easily perceived by a human, to an autonomous robot for it to function in an unfamiliar environment. However, a traditional SLAM system only creates a map that has no descriptive points of interest that may be useful for improved localization. In this project, a SLAM system is combined with a Text Detection and Recognition algorithm to provide a more descriptive visualization of the world. This composite system is designed and tested on the Jetson Xavier NX embedded platform. The ORB SLAM 2 algorithm was chosen for the SLAM system for its robustness and versatility. Then, the Efficient and Accurate Scene Text Detector (EAST) algorithm coupled with a Convolutional Recurrent Neural Network (CRNN) Scene Text Recognition was used to provide an efficient natural scene text detection and recognition. Bachelor of Engineering (Computer Engineering) 2022-05-11T01:06:35Z 2022-05-11T01:06:35Z 2022 Final Year Project (FYP) Syahir Toriman (2022). Hybrid SLAM and object recognition on an embedded platform. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157236 https://hdl.handle.net/10356/157236 en SCSE21-0006 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Hardware Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Syahir Toriman Hybrid SLAM and object recognition on an embedded platform |
| title | Hybrid SLAM and object recognition on an embedded platform |
| title_full | Hybrid SLAM and object recognition on an embedded platform |
| title_fullStr | Hybrid SLAM and object recognition on an embedded platform |
| title_full_unstemmed | Hybrid SLAM and object recognition on an embedded platform |
| title_short | Hybrid SLAM and object recognition on an embedded platform |
| title_sort | hybrid slam and object recognition on an embedded platform |
| topic | Engineering::Computer science and engineering::Hardware Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision |
| url | https://hdl.handle.net/10356/157236 |
| work_keys_str_mv | AT syahirtoriman hybridslamandobjectrecognitiononanembeddedplatform |