Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV

An autonomous underwater vehicle (AUV) has to solve two essential problems in underwater environment, namely, localization and mapping. SLAM is one novel solution to estimate locations and maps simultaneously based on motion models and sensor measurements. Sparse extended information filter (SEIF) i...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yue Shen [verfasserIn] Hao Zhang [verfasserIn] Bo He [verfasserIn] Tianhong Yan [verfasserIn] Yang Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Übergeordnetes Werk:	In: Mathematical Problems in Engineering - Hindawi Limited, 2002, (2015)
Übergeordnetes Werk:	year:2015

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1155/2015/752360

Katalog-ID:	DOAJ012335150

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allfields	10.1155/2015/752360 doi (DE-627)DOAJ012335150 (DE-599)DOAJ55ba6102652547299a568f240c8f0689 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Yue Shen verfasserin aut Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An autonomous underwater vehicle (AUV) has to solve two essential problems in underwater environment, namely, localization and mapping. SLAM is one novel solution to estimate locations and maps simultaneously based on motion models and sensor measurements. Sparse extended information filter (SEIF) is an effective algorithm to reduce storage and computational costs of large-scale maps in the SLAM problem. However, there exists the inconsistency in the SEIF since the rank of the observability matrix of linearized error-state model in SLAM system is higher than that of the nonlinear SLAM system. By analyzing the consistency of the SEIF-based SLAM from the perspective of observability, a SLAM based on SEIF with consistency constraint (SEIF-CC SLAM) is developed to improve the estimator’s consistency. The proposed algorithm uses the first-ever available estimates to calculate SEIF Jacobians for each of the state variables, called the First Estimates Jacobian (FEJ). Then, the linearized error-state model can keep the same observability as the underlying nonlinear SLAM system. The capability of autonomous navigation with the proposed algorithm is validated through simulations experiments and sea trials for a C-Ranger AUV. Experimental results show that the proposed SEIF-CC SLAM algorithm yields more consistent and accurate estimates compared with the SEIF-based SLAM. Engineering (General). Civil engineering (General) Mathematics Hao Zhang verfasserin aut Bo He verfasserin aut Tianhong Yan verfasserin aut Yang Liu verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2015) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2015 https://doi.org/10.1155/2015/752360 kostenfrei https://doaj.org/article/55ba6102652547299a568f240c8f0689 kostenfrei http://dx.doi.org/10.1155/2015/752360 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2015
spelling	10.1155/2015/752360 doi (DE-627)DOAJ012335150 (DE-599)DOAJ55ba6102652547299a568f240c8f0689 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Yue Shen verfasserin aut Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An autonomous underwater vehicle (AUV) has to solve two essential problems in underwater environment, namely, localization and mapping. SLAM is one novel solution to estimate locations and maps simultaneously based on motion models and sensor measurements. Sparse extended information filter (SEIF) is an effective algorithm to reduce storage and computational costs of large-scale maps in the SLAM problem. However, there exists the inconsistency in the SEIF since the rank of the observability matrix of linearized error-state model in SLAM system is higher than that of the nonlinear SLAM system. By analyzing the consistency of the SEIF-based SLAM from the perspective of observability, a SLAM based on SEIF with consistency constraint (SEIF-CC SLAM) is developed to improve the estimator’s consistency. The proposed algorithm uses the first-ever available estimates to calculate SEIF Jacobians for each of the state variables, called the First Estimates Jacobian (FEJ). Then, the linearized error-state model can keep the same observability as the underlying nonlinear SLAM system. The capability of autonomous navigation with the proposed algorithm is validated through simulations experiments and sea trials for a C-Ranger AUV. Experimental results show that the proposed SEIF-CC SLAM algorithm yields more consistent and accurate estimates compared with the SEIF-based SLAM. Engineering (General). Civil engineering (General) Mathematics Hao Zhang verfasserin aut Bo He verfasserin aut Tianhong Yan verfasserin aut Yang Liu verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2015) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2015 https://doi.org/10.1155/2015/752360 kostenfrei https://doaj.org/article/55ba6102652547299a568f240c8f0689 kostenfrei http://dx.doi.org/10.1155/2015/752360 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2015
allfields_unstemmed	10.1155/2015/752360 doi (DE-627)DOAJ012335150 (DE-599)DOAJ55ba6102652547299a568f240c8f0689 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Yue Shen verfasserin aut Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An autonomous underwater vehicle (AUV) has to solve two essential problems in underwater environment, namely, localization and mapping. SLAM is one novel solution to estimate locations and maps simultaneously based on motion models and sensor measurements. Sparse extended information filter (SEIF) is an effective algorithm to reduce storage and computational costs of large-scale maps in the SLAM problem. However, there exists the inconsistency in the SEIF since the rank of the observability matrix of linearized error-state model in SLAM system is higher than that of the nonlinear SLAM system. By analyzing the consistency of the SEIF-based SLAM from the perspective of observability, a SLAM based on SEIF with consistency constraint (SEIF-CC SLAM) is developed to improve the estimator’s consistency. The proposed algorithm uses the first-ever available estimates to calculate SEIF Jacobians for each of the state variables, called the First Estimates Jacobian (FEJ). Then, the linearized error-state model can keep the same observability as the underlying nonlinear SLAM system. The capability of autonomous navigation with the proposed algorithm is validated through simulations experiments and sea trials for a C-Ranger AUV. Experimental results show that the proposed SEIF-CC SLAM algorithm yields more consistent and accurate estimates compared with the SEIF-based SLAM. Engineering (General). Civil engineering (General) Mathematics Hao Zhang verfasserin aut Bo He verfasserin aut Tianhong Yan verfasserin aut Yang Liu verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2015) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2015 https://doi.org/10.1155/2015/752360 kostenfrei https://doaj.org/article/55ba6102652547299a568f240c8f0689 kostenfrei http://dx.doi.org/10.1155/2015/752360 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2015
allfieldsGer	10.1155/2015/752360 doi (DE-627)DOAJ012335150 (DE-599)DOAJ55ba6102652547299a568f240c8f0689 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Yue Shen verfasserin aut Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An autonomous underwater vehicle (AUV) has to solve two essential problems in underwater environment, namely, localization and mapping. SLAM is one novel solution to estimate locations and maps simultaneously based on motion models and sensor measurements. Sparse extended information filter (SEIF) is an effective algorithm to reduce storage and computational costs of large-scale maps in the SLAM problem. However, there exists the inconsistency in the SEIF since the rank of the observability matrix of linearized error-state model in SLAM system is higher than that of the nonlinear SLAM system. By analyzing the consistency of the SEIF-based SLAM from the perspective of observability, a SLAM based on SEIF with consistency constraint (SEIF-CC SLAM) is developed to improve the estimator’s consistency. The proposed algorithm uses the first-ever available estimates to calculate SEIF Jacobians for each of the state variables, called the First Estimates Jacobian (FEJ). Then, the linearized error-state model can keep the same observability as the underlying nonlinear SLAM system. The capability of autonomous navigation with the proposed algorithm is validated through simulations experiments and sea trials for a C-Ranger AUV. Experimental results show that the proposed SEIF-CC SLAM algorithm yields more consistent and accurate estimates compared with the SEIF-based SLAM. Engineering (General). Civil engineering (General) Mathematics Hao Zhang verfasserin aut Bo He verfasserin aut Tianhong Yan verfasserin aut Yang Liu verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2015) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2015 https://doi.org/10.1155/2015/752360 kostenfrei https://doaj.org/article/55ba6102652547299a568f240c8f0689 kostenfrei http://dx.doi.org/10.1155/2015/752360 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2015
allfieldsSound	10.1155/2015/752360 doi (DE-627)DOAJ012335150 (DE-599)DOAJ55ba6102652547299a568f240c8f0689 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Yue Shen verfasserin aut Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An autonomous underwater vehicle (AUV) has to solve two essential problems in underwater environment, namely, localization and mapping. SLAM is one novel solution to estimate locations and maps simultaneously based on motion models and sensor measurements. Sparse extended information filter (SEIF) is an effective algorithm to reduce storage and computational costs of large-scale maps in the SLAM problem. However, there exists the inconsistency in the SEIF since the rank of the observability matrix of linearized error-state model in SLAM system is higher than that of the nonlinear SLAM system. By analyzing the consistency of the SEIF-based SLAM from the perspective of observability, a SLAM based on SEIF with consistency constraint (SEIF-CC SLAM) is developed to improve the estimator’s consistency. The proposed algorithm uses the first-ever available estimates to calculate SEIF Jacobians for each of the state variables, called the First Estimates Jacobian (FEJ). Then, the linearized error-state model can keep the same observability as the underlying nonlinear SLAM system. The capability of autonomous navigation with the proposed algorithm is validated through simulations experiments and sea trials for a C-Ranger AUV. Experimental results show that the proposed SEIF-CC SLAM algorithm yields more consistent and accurate estimates compared with the SEIF-based SLAM. Engineering (General). Civil engineering (General) Mathematics Hao Zhang verfasserin aut Bo He verfasserin aut Tianhong Yan verfasserin aut Yang Liu verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2015) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2015 https://doi.org/10.1155/2015/752360 kostenfrei https://doaj.org/article/55ba6102652547299a568f240c8f0689 kostenfrei http://dx.doi.org/10.1155/2015/752360 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2015
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callnumber-first	T - Technology
author	Yue Shen
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title	Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV
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title_full	Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV
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title_sort	autonomous navigation based on seif with consistency constraint for c-ranger auv
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title_auth	Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV
abstract	An autonomous underwater vehicle (AUV) has to solve two essential problems in underwater environment, namely, localization and mapping. SLAM is one novel solution to estimate locations and maps simultaneously based on motion models and sensor measurements. Sparse extended information filter (SEIF) is an effective algorithm to reduce storage and computational costs of large-scale maps in the SLAM problem. However, there exists the inconsistency in the SEIF since the rank of the observability matrix of linearized error-state model in SLAM system is higher than that of the nonlinear SLAM system. By analyzing the consistency of the SEIF-based SLAM from the perspective of observability, a SLAM based on SEIF with consistency constraint (SEIF-CC SLAM) is developed to improve the estimator’s consistency. The proposed algorithm uses the first-ever available estimates to calculate SEIF Jacobians for each of the state variables, called the First Estimates Jacobian (FEJ). Then, the linearized error-state model can keep the same observability as the underlying nonlinear SLAM system. The capability of autonomous navigation with the proposed algorithm is validated through simulations experiments and sea trials for a C-Ranger AUV. Experimental results show that the proposed SEIF-CC SLAM algorithm yields more consistent and accurate estimates compared with the SEIF-based SLAM.
abstractGer	An autonomous underwater vehicle (AUV) has to solve two essential problems in underwater environment, namely, localization and mapping. SLAM is one novel solution to estimate locations and maps simultaneously based on motion models and sensor measurements. Sparse extended information filter (SEIF) is an effective algorithm to reduce storage and computational costs of large-scale maps in the SLAM problem. However, there exists the inconsistency in the SEIF since the rank of the observability matrix of linearized error-state model in SLAM system is higher than that of the nonlinear SLAM system. By analyzing the consistency of the SEIF-based SLAM from the perspective of observability, a SLAM based on SEIF with consistency constraint (SEIF-CC SLAM) is developed to improve the estimator’s consistency. The proposed algorithm uses the first-ever available estimates to calculate SEIF Jacobians for each of the state variables, called the First Estimates Jacobian (FEJ). Then, the linearized error-state model can keep the same observability as the underlying nonlinear SLAM system. The capability of autonomous navigation with the proposed algorithm is validated through simulations experiments and sea trials for a C-Ranger AUV. Experimental results show that the proposed SEIF-CC SLAM algorithm yields more consistent and accurate estimates compared with the SEIF-based SLAM.
abstract_unstemmed	An autonomous underwater vehicle (AUV) has to solve two essential problems in underwater environment, namely, localization and mapping. SLAM is one novel solution to estimate locations and maps simultaneously based on motion models and sensor measurements. Sparse extended information filter (SEIF) is an effective algorithm to reduce storage and computational costs of large-scale maps in the SLAM problem. However, there exists the inconsistency in the SEIF since the rank of the observability matrix of linearized error-state model in SLAM system is higher than that of the nonlinear SLAM system. By analyzing the consistency of the SEIF-based SLAM from the perspective of observability, a SLAM based on SEIF with consistency constraint (SEIF-CC SLAM) is developed to improve the estimator’s consistency. The proposed algorithm uses the first-ever available estimates to calculate SEIF Jacobians for each of the state variables, called the First Estimates Jacobian (FEJ). Then, the linearized error-state model can keep the same observability as the underlying nonlinear SLAM system. The capability of autonomous navigation with the proposed algorithm is validated through simulations experiments and sea trials for a C-Ranger AUV. Experimental results show that the proposed SEIF-CC SLAM algorithm yields more consistent and accurate estimates compared with the SEIF-based SLAM.
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title_short	Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV
url	https://doi.org/10.1155/2015/752360 https://doaj.org/article/55ba6102652547299a568f240c8f0689 http://dx.doi.org/10.1155/2015/752360 https://doaj.org/toc/1024-123X https://doaj.org/toc/1563-5147
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up_date	2024-07-04T00:39:37.243Z
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Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV

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