ORB-based optimal lost robot self-recovery

Due to various reasons, an autonomous mobile robot may deviate from its planned trajectory. If the robot deviates beyond its knowledge of the environment, it is essentially ‘lost’. To complete its programmed task, it is thus important for the robot to be able to perform ‘lost-recovery’, which is to...

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Main Author: Soo, Danny Hong Kit
Other Authors: Teoh Eam Khwang
Format: Final Year Project
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/71861
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-718612023-07-07T16:32:14Z ORB-based optimal lost robot self-recovery Soo, Danny Hong Kit Teoh Eam Khwang School of Electrical and Electronic Engineering A*STAR Institute for Infocomm Research Li Zhengguo DRNTU::Engineering::Electrical and electronic engineering Due to various reasons, an autonomous mobile robot may deviate from its planned trajectory. If the robot deviates beyond its knowledge of the environment, it is essentially ‘lost’. To complete its programmed task, it is thus important for the robot to be able to perform ‘lost-recovery’, which is to re-orientate itself back within the perimeters of a known environment. The key idea behind lost-recovery is the concept of simultaneous localization and mapping (SLAM), with place recognition being our area of interest. Existing place recognition techniques are based mostly on SIFT or SURF, which are highly accurate but computationally intensive. This report will explore the integration of a recently-developed descriptor, ORB (Oriented Fast and Rotated Brief), and the DBoW2 hierarchical tree structure to create an algorithm which guides a lost robot back to its programmed path through visual means. This report will start with a brief introduction about the lost robot problem, followed by a literature review on some of the existing work done on lost robot recovery and place recognition. Subsequent chapters will discuss in details the method proposed by the author and the results obtained from simulations in different environments. The report will conclude with analysis of results and suggestions for future work. Bachelor of Engineering 2017-05-19T06:33:43Z 2017-05-19T06:33:43Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/71861 en Nanyang Technological University 95 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
Soo, Danny Hong Kit
ORB-based optimal lost robot self-recovery
description Due to various reasons, an autonomous mobile robot may deviate from its planned trajectory. If the robot deviates beyond its knowledge of the environment, it is essentially ‘lost’. To complete its programmed task, it is thus important for the robot to be able to perform ‘lost-recovery’, which is to re-orientate itself back within the perimeters of a known environment. The key idea behind lost-recovery is the concept of simultaneous localization and mapping (SLAM), with place recognition being our area of interest. Existing place recognition techniques are based mostly on SIFT or SURF, which are highly accurate but computationally intensive. This report will explore the integration of a recently-developed descriptor, ORB (Oriented Fast and Rotated Brief), and the DBoW2 hierarchical tree structure to create an algorithm which guides a lost robot back to its programmed path through visual means. This report will start with a brief introduction about the lost robot problem, followed by a literature review on some of the existing work done on lost robot recovery and place recognition. Subsequent chapters will discuss in details the method proposed by the author and the results obtained from simulations in different environments. The report will conclude with analysis of results and suggestions for future work.
author2 Teoh Eam Khwang
author_facet Teoh Eam Khwang
Soo, Danny Hong Kit
format Final Year Project
author Soo, Danny Hong Kit
author_sort Soo, Danny Hong Kit
title ORB-based optimal lost robot self-recovery
title_short ORB-based optimal lost robot self-recovery
title_full ORB-based optimal lost robot self-recovery
title_fullStr ORB-based optimal lost robot self-recovery
title_full_unstemmed ORB-based optimal lost robot self-recovery
title_sort orb-based optimal lost robot self-recovery
publishDate 2017
url http://hdl.handle.net/10356/71861
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