Magnetic field-based mobile robot localization in repetitive environments

With the rapid development of mobile robots, a robust localization algorithm that could enable the robots to obtain accurate location within various kinds of environments is expected. However, the widely-used Global Navigation Satellite System (GNSS) may be unstable or even denied under certain circ...

Full description

Saved in:
Bibliographic Details
Main Author: Jiang, Zichen
Other Authors: Wang Dan Wei
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/140696
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-140696
record_format dspace
spelling sg-ntu-dr.10356-1406962023-07-04T16:44:19Z Magnetic field-based mobile robot localization in repetitive environments Jiang, Zichen Wang Dan Wei School of Electrical and Electronic Engineering EDWWANG@ntu.edu.sg Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics With the rapid development of mobile robots, a robust localization algorithm that could enable the robots to obtain accurate location within various kinds of environments is expected. However, the widely-used Global Navigation Satellite System (GNSS) may be unstable or even denied under certain circumstances. Consequently, new solutions need to be developed. Over the past several decades, place recognition and localization has proven to be a promising solution. Current robot localization methods can be broadly divided into two categories: infrastructure-based and infrastructure-free. For infrastructure-based methods, infrastructures such as WiFi, UWB, and QR codes need to be pre-installed in the environments to provide unique location information. However, the installation and maintenance of the infrastructures cost both resources and time. In contrast, infrastructure-free methods are independent of these infrastructures and only rely on the environment information collected by the onboard sensors to localize the robot. However, there are still challenging scenarios for infrastructure-free methods. Repetitive and symmetric environments, such as the corridors, carparks, and warehouses, usually have highly similar structures and few unique features. Infrastructure-free localization methods can be inaccurate or even fail in such environments. Among the infrastructure-free localization methods, magnetic field-based localization has proven to be a viable alternative to the LiDAR-based or vision-based localization due to its pervasiveness and ease of implementation. Therefore, this report will explore to solve the infrastructure-free mobile robot localization problem in repetitive and symmetric environments based on magnetic field information and the proposed improved particle filter algorithm. Master of Science (Computer Control and Automation) 2020-06-01T07:49:52Z 2020-06-01T07:49:52Z 2020 Thesis-Master by Coursework https://hdl.handle.net/10356/140696 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
spellingShingle Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
Jiang, Zichen
Magnetic field-based mobile robot localization in repetitive environments
description With the rapid development of mobile robots, a robust localization algorithm that could enable the robots to obtain accurate location within various kinds of environments is expected. However, the widely-used Global Navigation Satellite System (GNSS) may be unstable or even denied under certain circumstances. Consequently, new solutions need to be developed. Over the past several decades, place recognition and localization has proven to be a promising solution. Current robot localization methods can be broadly divided into two categories: infrastructure-based and infrastructure-free. For infrastructure-based methods, infrastructures such as WiFi, UWB, and QR codes need to be pre-installed in the environments to provide unique location information. However, the installation and maintenance of the infrastructures cost both resources and time. In contrast, infrastructure-free methods are independent of these infrastructures and only rely on the environment information collected by the onboard sensors to localize the robot. However, there are still challenging scenarios for infrastructure-free methods. Repetitive and symmetric environments, such as the corridors, carparks, and warehouses, usually have highly similar structures and few unique features. Infrastructure-free localization methods can be inaccurate or even fail in such environments. Among the infrastructure-free localization methods, magnetic field-based localization has proven to be a viable alternative to the LiDAR-based or vision-based localization due to its pervasiveness and ease of implementation. Therefore, this report will explore to solve the infrastructure-free mobile robot localization problem in repetitive and symmetric environments based on magnetic field information and the proposed improved particle filter algorithm.
author2 Wang Dan Wei
author_facet Wang Dan Wei
Jiang, Zichen
format Thesis-Master by Coursework
author Jiang, Zichen
author_sort Jiang, Zichen
title Magnetic field-based mobile robot localization in repetitive environments
title_short Magnetic field-based mobile robot localization in repetitive environments
title_full Magnetic field-based mobile robot localization in repetitive environments
title_fullStr Magnetic field-based mobile robot localization in repetitive environments
title_full_unstemmed Magnetic field-based mobile robot localization in repetitive environments
title_sort magnetic field-based mobile robot localization in repetitive environments
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/140696
_version_ 1772825566114742272