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Title: Hybrid SLAM and object recognition on an embedded platform
Authors: Syahir Toriman
Keywords: Engineering::Computer science and engineering::Hardware
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Syahir Toriman (2022). Hybrid SLAM and object recognition on an embedded platform. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: SCSE21-0006
Abstract: 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.
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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