Visual inertial odometry(VIO)problems have been extensively investigated in recent years.Existing VIO methods usually consider the localization or navigation issues of robots or autonomous vehicles in relatively small...Visual inertial odometry(VIO)problems have been extensively investigated in recent years.Existing VIO methods usually consider the localization or navigation issues of robots or autonomous vehicles in relatively small areas.This paper considers the problem of vision-aided inertial navigation(VIN)for aircrafts equipped with a strapdown inertial navigation system(SINS)and a downward-viewing camera.This is different from the traditional VIO problems in a larger working area with more precise inertial sensors.The goal is to utilize visual information to aid SINS to improve the navigation performance.In the multistate constraint Kalman filter(MSCKF)framework,we introduce an anchor frame to construct necessary models and derive corresponding Jacobians to implement a VIN filter to directly update the position in the Earth-centered Earth-fixed(ECEF)frame and the velocity and attitude in the local level frame by feature measurements.Due to its filtering-based property,the proposed method is naturally low computational demanding and is suitable for applications with high real-time requirements.Simulation and real-world data experiments demonstrate that the proposed method can considerably improve the navigation performance relative to the SINS.展开更多
Strapdown inertial navigation system(SINS)/celestial navigation system(CNS)integrated navigation is widely used to achieve long-time and high-precision autonomous navigation for aircraft.In general,SINS/CNS integrated...Strapdown inertial navigation system(SINS)/celestial navigation system(CNS)integrated navigation is widely used to achieve long-time and high-precision autonomous navigation for aircraft.In general,SINS/CNS integrated navigation can be divided into two integrated modes:loosely coupled integrated navigation and tightly coupled integrated navigation.Because the loosely coupled SINS/CNS integrated system is only available in the condition of at least three stars,the latter one is becoming a research hotspot.One major challenge of SINS/CNS integrated navigation is obtaining a high-precision horizon reference.To solve this problem,an innovative tightly coupled rotational SINS/CNS integrated navigation method is proposed.In this method,the rotational SINS error equation in the navigation frame is used as the state model,and the starlight vector and star altitude are used as measurements.Semi-physical simulations are conducted to test the performance of this integrated method.Results show that this tightly coupled rotational SINS/CNS method has the best navigation accuracy compared with SINS,rotational SINS,and traditional tightly coupled SINS/CNS integrated navigation method.展开更多
推导了捷联惯导系统(S IN S)误差方程和航位推算(DR)误差方程。建立了S IN S/DR组合导航离散卡尔曼滤波(KF)状态方程和量测方程。最后对S IN S/DR组合导航算法进行了仿真,仿真结果表明:组合系统中部分误差源能够被估计出来并且得到补偿...推导了捷联惯导系统(S IN S)误差方程和航位推算(DR)误差方程。建立了S IN S/DR组合导航离散卡尔曼滤波(KF)状态方程和量测方程。最后对S IN S/DR组合导航算法进行了仿真,仿真结果表明:组合系统中部分误差源能够被估计出来并且得到补偿,因而组合导航效果优于单独的S IN S或DR导航效果。展开更多
基金supported by the National Natural Science Foundation of China(61773306).
文摘Visual inertial odometry(VIO)problems have been extensively investigated in recent years.Existing VIO methods usually consider the localization or navigation issues of robots or autonomous vehicles in relatively small areas.This paper considers the problem of vision-aided inertial navigation(VIN)for aircrafts equipped with a strapdown inertial navigation system(SINS)and a downward-viewing camera.This is different from the traditional VIO problems in a larger working area with more precise inertial sensors.The goal is to utilize visual information to aid SINS to improve the navigation performance.In the multistate constraint Kalman filter(MSCKF)framework,we introduce an anchor frame to construct necessary models and derive corresponding Jacobians to implement a VIN filter to directly update the position in the Earth-centered Earth-fixed(ECEF)frame and the velocity and attitude in the local level frame by feature measurements.Due to its filtering-based property,the proposed method is naturally low computational demanding and is suitable for applications with high real-time requirements.Simulation and real-world data experiments demonstrate that the proposed method can considerably improve the navigation performance relative to the SINS.
基金supported by the National Natural Science Foundation of China(61722301)
文摘Strapdown inertial navigation system(SINS)/celestial navigation system(CNS)integrated navigation is widely used to achieve long-time and high-precision autonomous navigation for aircraft.In general,SINS/CNS integrated navigation can be divided into two integrated modes:loosely coupled integrated navigation and tightly coupled integrated navigation.Because the loosely coupled SINS/CNS integrated system is only available in the condition of at least three stars,the latter one is becoming a research hotspot.One major challenge of SINS/CNS integrated navigation is obtaining a high-precision horizon reference.To solve this problem,an innovative tightly coupled rotational SINS/CNS integrated navigation method is proposed.In this method,the rotational SINS error equation in the navigation frame is used as the state model,and the starlight vector and star altitude are used as measurements.Semi-physical simulations are conducted to test the performance of this integrated method.Results show that this tightly coupled rotational SINS/CNS method has the best navigation accuracy compared with SINS,rotational SINS,and traditional tightly coupled SINS/CNS integrated navigation method.
文摘推导了捷联惯导系统(S IN S)误差方程和航位推算(DR)误差方程。建立了S IN S/DR组合导航离散卡尔曼滤波(KF)状态方程和量测方程。最后对S IN S/DR组合导航算法进行了仿真,仿真结果表明:组合系统中部分误差源能够被估计出来并且得到补偿,因而组合导航效果优于单独的S IN S或DR导航效果。
