Twofeet : an infrastructure-free localization system for large indoor spaces and its applications using smartphones

This project implements TwoFeet, an infrastructure-free localization system designed for turn-by-turn navigation within large indoor spaces. While current approaches exist, many of these contending systems necessitate a complex infrastructure and do not take into consideration global scalability, en...

Full description

Saved in:
Bibliographic Details
Main Author: Loh, Alvin Kai Ip.
Other Authors: Ng Wee Keong
Format: Final Year Project
Language:English
Published: 2013
Subjects:
Online Access:http://hdl.handle.net/10356/51959
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-51959
record_format dspace
spelling sg-ntu-dr.10356-519592023-03-03T20:57:39Z Twofeet : an infrastructure-free localization system for large indoor spaces and its applications using smartphones Loh, Alvin Kai Ip. Ng Wee Keong School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Software::Software engineering This project implements TwoFeet, an infrastructure-free localization system designed for turn-by-turn navigation within large indoor spaces. While current approaches exist, many of these contending systems necessitate a complex infrastructure and do not take into consideration global scalability, energy efficiency, cost and ease of deployment. Utilizing solely the on-board accelerometer and magnetometer sensors available in modern smartphones, TwoFeet accurately localizes a user along a recommended shortest route. However, implementing this technique in human-scale environments is non-trivial as noise inherent to mobile sensors and complicated human kinetics conjure real world research problems. In order to compensate for inaccuracies arising from these challenges, an intelligent algorithm was designed to match detected user activity onto path signatures. This project also aimed to plug a gap left by similar systems that do not deal with user deviating from the displayed route. As part of the localization approach, a simple and inexpensive procedure was developed to map indoor environments without requiring expensive calibration efforts. Lastly, to provide a value-added experience for mobile users, this project also explored the integration of location-relevant features such as in-navigation advertising, crowd-sourced queue time information for F&B outlets and Where did I Park?. The resulting implementation was tested in a sample shopping mall, of which good localization precision and robustness was achieved. TwoFeet exhibited a 40% improvement in average accuracy compared with its closest related approach as well as successfully detected, bridged and corrected all cases of user divergence. Bachelor of Engineering (Computer Science) 2013-04-18T06:45:39Z 2013-04-18T06:45:39Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/51959 en Nanyang Technological University 69 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::Computer science and engineering::Software::Software engineering
spellingShingle DRNTU::Engineering::Computer science and engineering::Software::Software engineering
Loh, Alvin Kai Ip.
Twofeet : an infrastructure-free localization system for large indoor spaces and its applications using smartphones
description This project implements TwoFeet, an infrastructure-free localization system designed for turn-by-turn navigation within large indoor spaces. While current approaches exist, many of these contending systems necessitate a complex infrastructure and do not take into consideration global scalability, energy efficiency, cost and ease of deployment. Utilizing solely the on-board accelerometer and magnetometer sensors available in modern smartphones, TwoFeet accurately localizes a user along a recommended shortest route. However, implementing this technique in human-scale environments is non-trivial as noise inherent to mobile sensors and complicated human kinetics conjure real world research problems. In order to compensate for inaccuracies arising from these challenges, an intelligent algorithm was designed to match detected user activity onto path signatures. This project also aimed to plug a gap left by similar systems that do not deal with user deviating from the displayed route. As part of the localization approach, a simple and inexpensive procedure was developed to map indoor environments without requiring expensive calibration efforts. Lastly, to provide a value-added experience for mobile users, this project also explored the integration of location-relevant features such as in-navigation advertising, crowd-sourced queue time information for F&B outlets and Where did I Park?. The resulting implementation was tested in a sample shopping mall, of which good localization precision and robustness was achieved. TwoFeet exhibited a 40% improvement in average accuracy compared with its closest related approach as well as successfully detected, bridged and corrected all cases of user divergence.
author2 Ng Wee Keong
author_facet Ng Wee Keong
Loh, Alvin Kai Ip.
format Final Year Project
author Loh, Alvin Kai Ip.
author_sort Loh, Alvin Kai Ip.
title Twofeet : an infrastructure-free localization system for large indoor spaces and its applications using smartphones
title_short Twofeet : an infrastructure-free localization system for large indoor spaces and its applications using smartphones
title_full Twofeet : an infrastructure-free localization system for large indoor spaces and its applications using smartphones
title_fullStr Twofeet : an infrastructure-free localization system for large indoor spaces and its applications using smartphones
title_full_unstemmed Twofeet : an infrastructure-free localization system for large indoor spaces and its applications using smartphones
title_sort twofeet : an infrastructure-free localization system for large indoor spaces and its applications using smartphones
publishDate 2013
url http://hdl.handle.net/10356/51959
_version_ 1759854946344239104