Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor

Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide fast and accurate 6 degree-of-freedom state estimation, which is the fundamental requirement for robotic (especially, unmanned aerial vehicle) navigation tasks in Global Positioning System–...
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Autor*in:	Chang Liu [verfasserIn] Stephen D Prior [verfasserIn] WT Luke Teacy [verfasserIn] Martin Warner [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Übergeordnetes Werk:	In: Advances in Mechanical Engineering - SAGE Publishing, 2009, 8(2016)
Übergeordnetes Werk:	volume:8 ; year:2016

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1177/1687814016640996

Katalog-ID:	DOAJ072742925

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spelling	10.1177/1687814016640996 doi (DE-627)DOAJ072742925 (DE-599)DOAJ921d14f8062d4c4b9bf0e3939d085f99 DE-627 ger DE-627 rakwb eng TJ1-1570 Chang Liu verfasserin aut Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide fast and accurate 6 degree-of-freedom state estimation, which is the fundamental requirement for robotic (especially, unmanned aerial vehicle) navigation tasks in Global Positioning System–denied environments. This article presents a computationally efficient visual–inertial fusion algorithm, by separating orientation fusion from the position fusion process. The algorithm is designed to perform 6 degree-of-freedom state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localisation and mapping algorithm measurement. It also recovers the visual scale for the monocular visual-based simultaneous localisation and mapping. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, whereas the position fusion is based on a 13-state linear Kalman filter. The elimination of the magnetometer sensor avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the accelerometer is factory calibrated. The resulting algorithm shows a significant computational reduction over the conventional extended Kalman filter, with competitive accuracy. Moreover, the separation between orientation and position fusion processes enables the algorithm to be easily implemented onto two individual hardware elements and thus allows the two fusion processes to be executed concurrently. Mechanical engineering and machinery Stephen D Prior verfasserin aut WT Luke Teacy verfasserin aut Martin Warner verfasserin aut In Advances in Mechanical Engineering SAGE Publishing, 2009 8(2016) (DE-627)603487076 (DE-600)2501620-9 16878140 nnns volume:8 year:2016 https://doi.org/10.1177/1687814016640996 kostenfrei https://doaj.org/article/921d14f8062d4c4b9bf0e3939d085f99 kostenfrei https://doi.org/10.1177/1687814016640996 kostenfrei https://doaj.org/toc/1687-8140 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2706 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016
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allfieldsSound	10.1177/1687814016640996 doi (DE-627)DOAJ072742925 (DE-599)DOAJ921d14f8062d4c4b9bf0e3939d085f99 DE-627 ger DE-627 rakwb eng TJ1-1570 Chang Liu verfasserin aut Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide fast and accurate 6 degree-of-freedom state estimation, which is the fundamental requirement for robotic (especially, unmanned aerial vehicle) navigation tasks in Global Positioning System–denied environments. This article presents a computationally efficient visual–inertial fusion algorithm, by separating orientation fusion from the position fusion process. The algorithm is designed to perform 6 degree-of-freedom state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localisation and mapping algorithm measurement. It also recovers the visual scale for the monocular visual-based simultaneous localisation and mapping. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, whereas the position fusion is based on a 13-state linear Kalman filter. The elimination of the magnetometer sensor avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the accelerometer is factory calibrated. The resulting algorithm shows a significant computational reduction over the conventional extended Kalman filter, with competitive accuracy. Moreover, the separation between orientation and position fusion processes enables the algorithm to be easily implemented onto two individual hardware elements and thus allows the two fusion processes to be executed concurrently. Mechanical engineering and machinery Stephen D Prior verfasserin aut WT Luke Teacy verfasserin aut Martin Warner verfasserin aut In Advances in Mechanical Engineering SAGE Publishing, 2009 8(2016) (DE-627)603487076 (DE-600)2501620-9 16878140 nnns volume:8 year:2016 https://doi.org/10.1177/1687814016640996 kostenfrei https://doaj.org/article/921d14f8062d4c4b9bf0e3939d085f99 kostenfrei https://doi.org/10.1177/1687814016640996 kostenfrei https://doaj.org/toc/1687-8140 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2706 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016
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callnumber-first	T - Technology
author	Chang Liu
spellingShingle	Chang Liu misc TJ1-1570 misc Mechanical engineering and machinery Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor
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topic_title	TJ1-1570 Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor
topic	misc TJ1-1570 misc Mechanical engineering and machinery
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title	Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor
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title_full	Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor
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title_sort	computationally efficient visual–inertial sensor fusion for global positioning system–denied navigation on a small quadrotor
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title_auth	Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor
abstract	Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide fast and accurate 6 degree-of-freedom state estimation, which is the fundamental requirement for robotic (especially, unmanned aerial vehicle) navigation tasks in Global Positioning System–denied environments. This article presents a computationally efficient visual–inertial fusion algorithm, by separating orientation fusion from the position fusion process. The algorithm is designed to perform 6 degree-of-freedom state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localisation and mapping algorithm measurement. It also recovers the visual scale for the monocular visual-based simultaneous localisation and mapping. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, whereas the position fusion is based on a 13-state linear Kalman filter. The elimination of the magnetometer sensor avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the accelerometer is factory calibrated. The resulting algorithm shows a significant computational reduction over the conventional extended Kalman filter, with competitive accuracy. Moreover, the separation between orientation and position fusion processes enables the algorithm to be easily implemented onto two individual hardware elements and thus allows the two fusion processes to be executed concurrently.
abstractGer	Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide fast and accurate 6 degree-of-freedom state estimation, which is the fundamental requirement for robotic (especially, unmanned aerial vehicle) navigation tasks in Global Positioning System–denied environments. This article presents a computationally efficient visual–inertial fusion algorithm, by separating orientation fusion from the position fusion process. The algorithm is designed to perform 6 degree-of-freedom state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localisation and mapping algorithm measurement. It also recovers the visual scale for the monocular visual-based simultaneous localisation and mapping. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, whereas the position fusion is based on a 13-state linear Kalman filter. The elimination of the magnetometer sensor avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the accelerometer is factory calibrated. The resulting algorithm shows a significant computational reduction over the conventional extended Kalman filter, with competitive accuracy. Moreover, the separation between orientation and position fusion processes enables the algorithm to be easily implemented onto two individual hardware elements and thus allows the two fusion processes to be executed concurrently.
abstract_unstemmed	Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide fast and accurate 6 degree-of-freedom state estimation, which is the fundamental requirement for robotic (especially, unmanned aerial vehicle) navigation tasks in Global Positioning System–denied environments. This article presents a computationally efficient visual–inertial fusion algorithm, by separating orientation fusion from the position fusion process. The algorithm is designed to perform 6 degree-of-freedom state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localisation and mapping algorithm measurement. It also recovers the visual scale for the monocular visual-based simultaneous localisation and mapping. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, whereas the position fusion is based on a 13-state linear Kalman filter. The elimination of the magnetometer sensor avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the accelerometer is factory calibrated. The resulting algorithm shows a significant computational reduction over the conventional extended Kalman filter, with competitive accuracy. Moreover, the separation between orientation and position fusion processes enables the algorithm to be easily implemented onto two individual hardware elements and thus allows the two fusion processes to be executed concurrently.
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title_short	Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor
url	https://doi.org/10.1177/1687814016640996 https://doaj.org/article/921d14f8062d4c4b9bf0e3939d085f99 https://doaj.org/toc/1687-8140
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