Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred ...Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred to as outliers)are present in the SLAM front-end,the standard PGO algorithm fails catastrophically and can not return an accurate map.To address this issue,this letter proposes a novel algorithm that leverages classical optimization methods to effectively handle outliers.The proposed algorithm introduces a new formulation that incorporates a credibility factor model,which improves the robustness of the optimization process.Additionally,an innovative consistency classification algorithm is developed to detect outliers.Extensive experiments are conducted on multiple benchmark datasets to evaluate the consistency and accuracy of the proposed algorithm.展开更多
With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation sa...With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information continuously.However,when this system is applied to indoor or GNSS-denied environments,such as outdoor substations with strong electromagnetic interference and complex dense spaces,it is often unable to obtain high-precision GNSS positioning data.The positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios.This paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source optimization.Through the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.展开更多
Navigation and positioning is an important and challenging problem in many control engineering applications.It provides feedback information to design controllers for systems.In this paper,a bibliographical review on ...Navigation and positioning is an important and challenging problem in many control engineering applications.It provides feedback information to design controllers for systems.In this paper,a bibliographical review on factor graph based navigation and positioning is presented.More specifically,the sensor modeling,the factor graph optimization methods,and the topology factor based cooperative localization are reviewed.The navigation and positioning methods via factor graph are considered and classified.Focuses in the current research of factor graph based navigation and positioning are also discussed with emphasis on its practical application.The limitations of the existing methods,some solutions for future techniques,and recommendations are finally given.展开更多
In recent years,the Factor Graph Optimization(FGO)algorithm has gained a great attention in the feld of integrated navigation owing to its better positioning performance than the traditional flter-based approaches.How...In recent years,the Factor Graph Optimization(FGO)algorithm has gained a great attention in the feld of integrated navigation owing to its better positioning performance than the traditional flter-based approaches.However,the practical application of the FGO algorithm remains challenging due to its signifcant computational complexity and processing time consumption,especially for the case of limited storage and computation resources.In order to overcome the problem,we frst conduct a thorough analysis of the factor graph model for the Global Navigation Satellite System/Inertial Navigation System(GNSS/INS)integrated navigation.Then,based on the Incremental Smoothing and Mapping(iSAM),an Optimized iSAM(OiSAM)algorithm is proposed to efciently solve the optimization problem in FGO,with reducing computational load and required memory resources.For the re-linearization problem,we propose a novel Adaptive Joint Sliding Window Re-linearization(A-JSWR)algorithm combining periodic and on-demand re-linearization to further improve the efciency of OiSAM.Finally,the OiSAM-FGO method utilizing OiSAM and A-JSWR is presented for the GNSS/INS integrated navigation.The experiments on real-world datasets demonstrated that the OiSAM-FGO can reduce the time consumption of the optimization procedure by up to 52.24%,while achieving a performance equivalent to that of the State-of-the-Art(SOTA)FGO method and superior to the Extended Kalman Filter(EKF)method.展开更多
基金supported in part by the National Nature Science Foundation of China(62273239,62103283).
文摘Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred to as outliers)are present in the SLAM front-end,the standard PGO algorithm fails catastrophically and can not return an accurate map.To address this issue,this letter proposes a novel algorithm that leverages classical optimization methods to effectively handle outliers.The proposed algorithm introduces a new formulation that incorporates a credibility factor model,which improves the robustness of the optimization process.Additionally,an innovative consistency classification algorithm is developed to detect outliers.Extensive experiments are conducted on multiple benchmark datasets to evaluate the consistency and accuracy of the proposed algorithm.
基金supported in part by the Guangxi Power Grid Company’s 2023 Science and Technol-ogy Innovation Project(No.GXKJXM20230169)。
文摘With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information continuously.However,when this system is applied to indoor or GNSS-denied environments,such as outdoor substations with strong electromagnetic interference and complex dense spaces,it is often unable to obtain high-precision GNSS positioning data.The positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios.This paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source optimization.Through the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.
基金supported by the National Natural Science Foundation of China(No.61873207)the National Science and Technology Major Project,China(No.J2019-I-00210020)+2 种基金the Natural Science Basic Research Program of Shaanxi,China(No.2019JQ-344)the Science and Technology Program of Xi’an City,China(No.2019218314GXRC019CG020-GXYD19.3)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China。
文摘Navigation and positioning is an important and challenging problem in many control engineering applications.It provides feedback information to design controllers for systems.In this paper,a bibliographical review on factor graph based navigation and positioning is presented.More specifically,the sensor modeling,the factor graph optimization methods,and the topology factor based cooperative localization are reviewed.The navigation and positioning methods via factor graph are considered and classified.Focuses in the current research of factor graph based navigation and positioning are also discussed with emphasis on its practical application.The limitations of the existing methods,some solutions for future techniques,and recommendations are finally given.
文摘In recent years,the Factor Graph Optimization(FGO)algorithm has gained a great attention in the feld of integrated navigation owing to its better positioning performance than the traditional flter-based approaches.However,the practical application of the FGO algorithm remains challenging due to its signifcant computational complexity and processing time consumption,especially for the case of limited storage and computation resources.In order to overcome the problem,we frst conduct a thorough analysis of the factor graph model for the Global Navigation Satellite System/Inertial Navigation System(GNSS/INS)integrated navigation.Then,based on the Incremental Smoothing and Mapping(iSAM),an Optimized iSAM(OiSAM)algorithm is proposed to efciently solve the optimization problem in FGO,with reducing computational load and required memory resources.For the re-linearization problem,we propose a novel Adaptive Joint Sliding Window Re-linearization(A-JSWR)algorithm combining periodic and on-demand re-linearization to further improve the efciency of OiSAM.Finally,the OiSAM-FGO method utilizing OiSAM and A-JSWR is presented for the GNSS/INS integrated navigation.The experiments on real-world datasets demonstrated that the OiSAM-FGO can reduce the time consumption of the optimization procedure by up to 52.24%,while achieving a performance equivalent to that of the State-of-the-Art(SOTA)FGO method and superior to the Extended Kalman Filter(EKF)method.
