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
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
High-density urban environments severely impair smartphone Global Navigation Satellite System(GNSS)positioning due to Non-Line-of-Sight(NLOS)signals and limited satellite visibility,leading to reduced accuracy and con...High-density urban environments severely impair smartphone Global Navigation Satellite System(GNSS)positioning due to Non-Line-of-Sight(NLOS)signals and limited satellite visibility,leading to reduced accuracy and continuity.Three-Dimensional Map-aided(3DMA)GNSS methods partially solve the problems but still much rely on noisy pseudorange measurements,while the resolution of carrier-phase ambiguities remain challenging,limiting their robustness in complex urban areas.To overcome these challenges,this study introduces a novel Factor Graph Optimization(FGO)framework that tightly integrates 3D map constraints with multiple GNSS observations.First,a Shadow Matching(SDM)scoring strategy is proposed by incorporating Time-Diferenced Carrier Phase(TDCP)constraints.Second,a map-matching probability approach is applied to identify a unique candidate road segment,thereby reducing solution ambiguity.Third,a Random Sample Consensus(RANSAC)-based region growing clustering algorithm is designed to manage multimodal high-score points and ensure unique clustering.Finally,a factor graph model is constructed that fuses pseudorange,Doppler,and TDCP observations with 3D map constraints,signifcantly enhancing positioning accuracy and stability.Field experiments in typical urban scenarios show that the proposed method outperforms existing SDM techniques such as road constraint and region-growing clustering,as well as advanced GNSS optimization frameworks,in terms of both positioning accuracy and trajectory continuity.Specifcally,the proportion of horizontal positioning errors within 3 m and 5 m reached 76.7%and 93.1%,respectively,substantially exceeding those achieved by the advanced GNSS multi-source fusion framework(63.4%and 79.3%).展开更多
Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are eas...Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are easy to be blocked or reflected by obstacles such as walls and furniture.A resilient tightly-coupled inertial navigation system(INS)/UWB integration is proposed and implemented for indoor UAV navigation in this paper.A factor graph optimization(FGO)method enhanced by resilient stochastic model is established to cope with the indoor challenging scenarios.To deal with the impact of UWB non-line-of-sight(NLOS)signals and noise uncertainty,the conventional neural net-works(CNNs)are introduced into the stochastic modelling to improve the resilience and reliability of the integration.Based on the status that the UWB features are limited,a‘two-phase'CNNs structure was designed and implemented:one for signal classification and the other one for measurement noise prediction.The proposed resilient FGO method is tested on flighting UAV platform under actual indoor challenging scenario.Compared to classical FGO method,the overall positioning errors can be decreased from about 0.60 m to centimeter-level under signal block and reflection scenarios.The superiority of resilient FGO which effectively verified in constrained environment is pretty important for positioning accuracy and integrity for indoor navigation task.展开更多
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.
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.
基金supported in part by the National Natural Science Foundation of China(Grants 42394060,42394065 and 42274020)the Science and Technology Planning Project of Jiangsu Province(Grant BE2023692)+2 种基金supported by the Fundamental Research Funds for the Central Universities(Grants 2025-00046)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant KYCX25_2890)the Graduate Innovation Program of China University of Mining and Technology(Grant 2025WLKXJ200).
文摘High-density urban environments severely impair smartphone Global Navigation Satellite System(GNSS)positioning due to Non-Line-of-Sight(NLOS)signals and limited satellite visibility,leading to reduced accuracy and continuity.Three-Dimensional Map-aided(3DMA)GNSS methods partially solve the problems but still much rely on noisy pseudorange measurements,while the resolution of carrier-phase ambiguities remain challenging,limiting their robustness in complex urban areas.To overcome these challenges,this study introduces a novel Factor Graph Optimization(FGO)framework that tightly integrates 3D map constraints with multiple GNSS observations.First,a Shadow Matching(SDM)scoring strategy is proposed by incorporating Time-Diferenced Carrier Phase(TDCP)constraints.Second,a map-matching probability approach is applied to identify a unique candidate road segment,thereby reducing solution ambiguity.Third,a Random Sample Consensus(RANSAC)-based region growing clustering algorithm is designed to manage multimodal high-score points and ensure unique clustering.Finally,a factor graph model is constructed that fuses pseudorange,Doppler,and TDCP observations with 3D map constraints,signifcantly enhancing positioning accuracy and stability.Field experiments in typical urban scenarios show that the proposed method outperforms existing SDM techniques such as road constraint and region-growing clustering,as well as advanced GNSS optimization frameworks,in terms of both positioning accuracy and trajectory continuity.Specifcally,the proportion of horizontal positioning errors within 3 m and 5 m reached 76.7%and 93.1%,respectively,substantially exceeding those achieved by the advanced GNSS multi-source fusion framework(63.4%and 79.3%).
基金National Natural Science Foundation of China(Grant No.62203111)the Open Research Fund of State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University(Grant No.21P01)the Foundation of Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology,Ministry of Education,China(Grant No.SEU-MIAN-202101)to provide fund for conducting experiments。
文摘Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are easy to be blocked or reflected by obstacles such as walls and furniture.A resilient tightly-coupled inertial navigation system(INS)/UWB integration is proposed and implemented for indoor UAV navigation in this paper.A factor graph optimization(FGO)method enhanced by resilient stochastic model is established to cope with the indoor challenging scenarios.To deal with the impact of UWB non-line-of-sight(NLOS)signals and noise uncertainty,the conventional neural net-works(CNNs)are introduced into the stochastic modelling to improve the resilience and reliability of the integration.Based on the status that the UWB features are limited,a‘two-phase'CNNs structure was designed and implemented:one for signal classification and the other one for measurement noise prediction.The proposed resilient FGO method is tested on flighting UAV platform under actual indoor challenging scenario.Compared to classical FGO method,the overall positioning errors can be decreased from about 0.60 m to centimeter-level under signal block and reflection scenarios.The superiority of resilient FGO which effectively verified in constrained environment is pretty important for positioning accuracy and integrity for indoor navigation task.
