Because of its high-precision,low-cost and easy-operation,Precise Point Positioning(PPP)becomes a potential and attractive positioning technique that can be applied to self-driving cars and drones.However,the reliabil...Because of its high-precision,low-cost and easy-operation,Precise Point Positioning(PPP)becomes a potential and attractive positioning technique that can be applied to self-driving cars and drones.However,the reliability and availability of PPP will be significantly degraded in the extremely difficult conditions where Global Navigation Satellite System(GNSS)signals are blocked frequently.Inertial Navigation System(INS)has been integrated with GNSS to ameliorate such situations in the last decades.Recently,the Visual-Inertial Navigation Systems(VINS)with favorable complementary characteristics is demonstrated to realize a more stable and accurate local position estimation than the INS-only.Nevertheless,the system still must rely on the global positions to eliminate the accumulated errors.In this contribution,we present a semi-tight coupling framework of multi-GNSS PPP and Stereo VINS(S-VINS),which achieves the bidirectional location transfer and sharing in two separate navigation systems.In our approach,the local positions,produced by S-VINS are integrated with multi-GNSS PPP through a graph-optimization based method.Furthermore,the accurate forecast positions with S-VINS are fed back to assist PPP in GNSS-challenged environments.The statistical analysis of a GNSS outage simulation test shows that the S-VINS mode can effectively suppress the degradation of positioning accuracy compared with the INS-only mode.We also carried out a vehicle-borne experiment collecting multi-sensor data in a GNSS-challenged environment.For the complex driving environment,the PPP positioning capability is significantly improved with the aiding of S-VINS.The 3D positioning accuracy is improved by 49.0%for Global Positioning System(GPS),40.3%for GPS+GLOANSS(Global Navigation Satellite System),45.6%for GPS+BDS(BeiDou navigation satellite System),and 51.2%for GPS+GLONASS+BDS.On this basis,the solution with the semi-tight coupling scheme of multi-GNSS PPP/S-VINS achieves the improvements of 41.8-60.6%in 3D position-ing accuracy compared with the multi-GNSS PPP/INS solutions.展开更多
Accurate positioning and navigation play a vital role in vehicle-related applications,such as autonomous driving and precision agriculture.With the rapid development of Global Navigation Satellite Systems(GNSS),Precis...Accurate positioning and navigation play a vital role in vehicle-related applications,such as autonomous driving and precision agriculture.With the rapid development of Global Navigation Satellite Systems(GNSS),Precise Point Positioning(PPP)technique,as a global positioning solution,has been widely applied due to its convenient operation.Nevertheless,the performance of PPP is severely affected by signal interference,especially in GNSS-challenged environments.Inertial Navigation System(INS)aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments,but suffers from degradation during GNSS outages.LiDAR(Laser Imaging,Detection,and Ranging)-Inertial Odometry(LIO),which has performed well in local navigation,can restrain the divergence of Inertial Measurement Units(IMU).However,in long-range navigation,error accumulation is inevitable if no external aids are applied.To improve vehicle navigation performance,we proposed a tightly coupled GNSS PPP/INS/LiDAR(GIL)integration method,which tightly integrates the raw measurements from multi-GNSS PPP,Micro-Electro-Mechanical System(MEMS)-IMU,and LiDAR to achieve high-accuracy and reliable navigation in urban environments.Several experiments were conducted to evaluate this method.The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors(RMSEs)of the proposed GIL method have the improvements of 63.0%,51.3%,and 62.2%in east,north,and vertical components,respectively.The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment(i.e.,the environment with GNSS signals partly-blocked)and meter-level positioning accuracy in GNSS difficult environment(i.e.,the environment with GNSS hardly used).Besides,the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.展开更多
基金the National Natural Science Foundation of China(Grant No.41774030,Grant 41974027)the Hubei Province Natural Science Foundation of China(Grant No.2018CFA081)+1 种基金the National Youth Thousand Talents Program,the frontier project of basic application from Wuhan science and technology bureau(Grant No.2019010701011395)the Sino-German mobility programme(Grant No.M-0054).
文摘Because of its high-precision,low-cost and easy-operation,Precise Point Positioning(PPP)becomes a potential and attractive positioning technique that can be applied to self-driving cars and drones.However,the reliability and availability of PPP will be significantly degraded in the extremely difficult conditions where Global Navigation Satellite System(GNSS)signals are blocked frequently.Inertial Navigation System(INS)has been integrated with GNSS to ameliorate such situations in the last decades.Recently,the Visual-Inertial Navigation Systems(VINS)with favorable complementary characteristics is demonstrated to realize a more stable and accurate local position estimation than the INS-only.Nevertheless,the system still must rely on the global positions to eliminate the accumulated errors.In this contribution,we present a semi-tight coupling framework of multi-GNSS PPP and Stereo VINS(S-VINS),which achieves the bidirectional location transfer and sharing in two separate navigation systems.In our approach,the local positions,produced by S-VINS are integrated with multi-GNSS PPP through a graph-optimization based method.Furthermore,the accurate forecast positions with S-VINS are fed back to assist PPP in GNSS-challenged environments.The statistical analysis of a GNSS outage simulation test shows that the S-VINS mode can effectively suppress the degradation of positioning accuracy compared with the INS-only mode.We also carried out a vehicle-borne experiment collecting multi-sensor data in a GNSS-challenged environment.For the complex driving environment,the PPP positioning capability is significantly improved with the aiding of S-VINS.The 3D positioning accuracy is improved by 49.0%for Global Positioning System(GPS),40.3%for GPS+GLOANSS(Global Navigation Satellite System),45.6%for GPS+BDS(BeiDou navigation satellite System),and 51.2%for GPS+GLONASS+BDS.On this basis,the solution with the semi-tight coupling scheme of multi-GNSS PPP/S-VINS achieves the improvements of 41.8-60.6%in 3D position-ing accuracy compared with the multi-GNSS PPP/INS solutions.
基金the National Natural Science Foundation of China(Grant 41774030,Grant 41974027,and Grant 41974029)the Hubei Province Natural Science Foundation of China(Grant 2018CFA081)+1 种基金the frontier project of basic application from Wuhan science and technology bureau(Grant 2019010701011395)the Sino-German mobility programme(Grant No.M-0054).
文摘Accurate positioning and navigation play a vital role in vehicle-related applications,such as autonomous driving and precision agriculture.With the rapid development of Global Navigation Satellite Systems(GNSS),Precise Point Positioning(PPP)technique,as a global positioning solution,has been widely applied due to its convenient operation.Nevertheless,the performance of PPP is severely affected by signal interference,especially in GNSS-challenged environments.Inertial Navigation System(INS)aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments,but suffers from degradation during GNSS outages.LiDAR(Laser Imaging,Detection,and Ranging)-Inertial Odometry(LIO),which has performed well in local navigation,can restrain the divergence of Inertial Measurement Units(IMU).However,in long-range navigation,error accumulation is inevitable if no external aids are applied.To improve vehicle navigation performance,we proposed a tightly coupled GNSS PPP/INS/LiDAR(GIL)integration method,which tightly integrates the raw measurements from multi-GNSS PPP,Micro-Electro-Mechanical System(MEMS)-IMU,and LiDAR to achieve high-accuracy and reliable navigation in urban environments.Several experiments were conducted to evaluate this method.The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors(RMSEs)of the proposed GIL method have the improvements of 63.0%,51.3%,and 62.2%in east,north,and vertical components,respectively.The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment(i.e.,the environment with GNSS signals partly-blocked)and meter-level positioning accuracy in GNSS difficult environment(i.e.,the environment with GNSS hardly used).Besides,the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.
