Towards accurate object localization with smartphones

In this study, we explore the possibility of locating remote objects via cameras together with built-in inertial sensors of off-the-shelf smartphones. Our solution, CamLoc, enables a user taking two photos of an object using a smartphone at a fixed location and immediately knowing the location of th...

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Main Authors: SHANGGUAN, Longfei, ZHOU, Zimu, YANG, Zheng, LIU, Kebin, LI, Zhenjiang, LIU, Yunhao
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Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/sis_research/4607
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spelling sg-smu-ink.sis_research-56102019-12-26T06:12:03Z Towards accurate object localization with smartphones SHANGGUAN, Longfei ZHOU, Zimu YANG, Zheng LIU, Kebin LI, Zhenjiang LIU, Yunhao In this study, we explore the possibility of locating remote objects via cameras together with built-in inertial sensors of off-the-shelf smartphones. Our solution, CamLoc, enables a user taking two photos of an object using a smartphone at a fixed location and immediately knowing the location of the object in global coordinates, thus facilitating myriad location-based services. Such usage is user-friendly but error prone. We devise several techniques to mitigate the errors caused by cheap and noisy sensors, upgrading the positioning accuracy to an applicable level. We prototype CamLoc on Android OS, and evaluate its performance across different scenarios with various building densities. Experiment results show that our system achieves 89 percent and 72 percent physical location mapping accuracy in rural and downtown areas, respectively, which is competitive with existing solutions. 2013-09-17T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/4607 info:doi/10.1109/TPDS.2013.236 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Software Engineering
spellingShingle Software Engineering
SHANGGUAN, Longfei
ZHOU, Zimu
YANG, Zheng
LIU, Kebin
LI, Zhenjiang
LIU, Yunhao
Towards accurate object localization with smartphones
description In this study, we explore the possibility of locating remote objects via cameras together with built-in inertial sensors of off-the-shelf smartphones. Our solution, CamLoc, enables a user taking two photos of an object using a smartphone at a fixed location and immediately knowing the location of the object in global coordinates, thus facilitating myriad location-based services. Such usage is user-friendly but error prone. We devise several techniques to mitigate the errors caused by cheap and noisy sensors, upgrading the positioning accuracy to an applicable level. We prototype CamLoc on Android OS, and evaluate its performance across different scenarios with various building densities. Experiment results show that our system achieves 89 percent and 72 percent physical location mapping accuracy in rural and downtown areas, respectively, which is competitive with existing solutions.
format text
author SHANGGUAN, Longfei
ZHOU, Zimu
YANG, Zheng
LIU, Kebin
LI, Zhenjiang
LIU, Yunhao
author_facet SHANGGUAN, Longfei
ZHOU, Zimu
YANG, Zheng
LIU, Kebin
LI, Zhenjiang
LIU, Yunhao
author_sort SHANGGUAN, Longfei
title Towards accurate object localization with smartphones
title_short Towards accurate object localization with smartphones
title_full Towards accurate object localization with smartphones
title_fullStr Towards accurate object localization with smartphones
title_full_unstemmed Towards accurate object localization with smartphones
title_sort towards accurate object localization with smartphones
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/4607
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