Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual sensors.Hybrid VIO is an extended Kalman filter-...Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual sensors.Hybrid VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter(MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, onedimensional inverse depth parametrization is utilized to parametrize the augmented feature state.This modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene,a novel closed-form Zero velocity UPda Te(ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.展开更多
Foot-mounted pedestrian navigation system(PNS)is a common solution to pedestrian navigation using micro-electro mechanical system(MEMS)inertial sensors.The inherent problems of inertial navigation system(INS)by the tr...Foot-mounted pedestrian navigation system(PNS)is a common solution to pedestrian navigation using micro-electro mechanical system(MEMS)inertial sensors.The inherent problems of inertial navigation system(INS)by the traditional algorithm,such as the accumulated errors and the lack of observation of heading and altitude information,have become obstacles to the application and development of the PNS.In this paper,we introduce a heuristic heading constraint method.First of all,according to the movement characteristics of human gait,we use the generalized likelihood ratio test(GLRT)detector and introduce a time threshold to classify the human gait,so that we can effectively identify the stationary state of the foot.In addition,based on zero velocity update(ZUPT)and zero angular rate update(ZARU),the cumulative error of the inertial measurement unit(IMU)is limited and corrected,and then a heuristic heading estimation is used to constrain and correct the heading of the pedestrian.After simulation and experiments with low-cost IMU,the method is proved to reduce the localization error of end-point to less than 1%of the total distance,and it has great value in application.展开更多
Timely and relatively accurate detection of the center position of a near-landfalling typhoon is essential for weather forecasts, warning, disaster preparedness and data assimilation. Due to the vortex like nature of ...Timely and relatively accurate detection of the center position of a near-landfalling typhoon is essential for weather forecasts, warning, disaster preparedness and data assimilation. Due to the vortex like nature of the flow near the typhoon center, the Zero Radial Velocity Line(ZRVL) of a Doppler radar extends through the typhoon center and the radar site. This paper proposes a method to detect the typhoon center by intersecting the ZRVLs of a dual-Doppler radar system. Simulation with Rankine Vortex model shows the validity of this method. This paper also evaluated the method by locating the centers of Typhoon FUNG-WONG(No. 201416) which impacted the east coastal regions of China from the afternoon of 22 September to the morning of 23 September, 2014 BST(Beijing Standard Time). During this period, the centers of the coastal typhoon were precisely positioned by intersecting the ZRVLs of two Doppler radars. The positioning accuracy of this method is comparable to that from Automatic Weather Stations(AWS) wind data. This method is suitable for areas where AWS data are unavailable, supposing radars are apart 100 km with a maximum unambiguous distance of 148 km, this technique can cover most regions effectively.展开更多
This paper investigates the magnetic field component impact on cathode spots motion trajectory and the mechanism of periodic contraction.Electromagnetic coils and permanent magnets were installed at the different side...This paper investigates the magnetic field component impact on cathode spots motion trajectory and the mechanism of periodic contraction.Electromagnetic coils and permanent magnets were installed at the different sides of cathode surface,the photographs of cathode spots motion trajectory were captured by a camera.Increasing the number of magnets and decreasing the distance between magnets and cathode both lead to enhancing cathode spots motion velocity.Radii of cathode spots trajectory decrease gradually with the increasing of electromagnetic coil＇s current,from 40 mm at 0 A to 10 mm at 2.7 A.Parallel magnetic field component intensity influence the speed of cathode spots rotate motion,and perpendicular magnetic field component drives spots drift in the radial direction.Cathode spot＇s radial drift is controlled by changing the location of the ‘zero line＇ where perpendicular magnetic component shifts direction and the radius of cathode spots trajectory almost equal to ‘zero line＇.展开更多
In outdoor environments, GPS is often used for pedestrian navigation by utilizing its signals for position computation, but in indoor or semi-obstructed environments, GPS signals are often unavailable. Therefore, pede...In outdoor environments, GPS is often used for pedestrian navigation by utilizing its signals for position computation, but in indoor or semi-obstructed environments, GPS signals are often unavailable. Therefore, pedestrian navigation for these environments should be realized by the integration of GPS and inertial navigation system （INS）. However, the lowcost INS could induce errors that may result in a large position drift. The problem can be minimized by mounting the sensors on the pedestrian＇s foot, using zero velocity update （ZUPT） method with the standard navigation algorithm to restrict the error growth. However, heading drift still remains despite using ZUPT measurements since the heading error is unobservable. Also, tbot mounted INS suffers from the initialization ambiguity of position and heading from GPS. In this paper, a novel algorithm is developed to mitigate the heading drift problem when using ZUPT. The method uses building lay- out to aid the heading measurement in Kalman filter, and it could also be combined for the initial- ization. The algorithm has been investigated with real field trials using the low cost Microstrain 3DM-GX3-25 inertial sensor, a Leica GS10 GPS receiver and a uBlox EVK-6T GPS receiver. It could be concluded that the proposed method offers a significant improvement in position accuracy for the long period, allowing pedestrian navigation for nearly40 min with mean position error less than 2.8 m. This method also has a considerable effect on the accuracy of the initialization.展开更多
Dead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change.Pedestrian Dead Reckoning(PDR)applie...Dead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change.Pedestrian Dead Reckoning(PDR)applies this concept to walking persons.The method can be used to track someone's movement in a building after a known landmark like the building's entrance is registered.Here,the movement of a foot and the corresponding direction change is measured and summed up,to infer the current position.Measuring and integrating the corresponding physical parameters,e.g.using inertial sensors,introduces small errors that accumulate quickly into large distance errors.Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths.In this paper,we use building maps to improve localization based on a single foot-mounted inertial sensor.We describe our localization method using zero velocity updates to accurately compute the length of individual steps and a Madgwick filter to determine the step orientation.Even though the computation of individual steps is quite accurate,small errors still accumulate in the long term.We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks,such as orientation and displacement drift,and discuss restrictions and disadvantages of these algorithms.We also present a method of deriving the initial position and orientation from GPS measurements.We verify our PDR correction methods analyzing the corrected and raw trajectories of six participants walking four routes of varying length and complexity through an office building,walking each route three times.Our quantitative results show an endpoint accuracy improvement of up to 60%when using likely paths and 23%when using unlikely paths.However,both approaches can also decrease accuracy in certain scenarios.We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods.展开更多
基金supported by the National Key Research and Development Program of China(Nos.2016YFB0502004,2017YFC0821102)。
文摘Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual sensors.Hybrid VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter(MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, onedimensional inverse depth parametrization is utilized to parametrize the augmented feature state.This modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene,a novel closed-form Zero velocity UPda Te(ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.
基金This work was supported by the National Natural Science Foundation of China(61803278).
文摘Foot-mounted pedestrian navigation system(PNS)is a common solution to pedestrian navigation using micro-electro mechanical system(MEMS)inertial sensors.The inherent problems of inertial navigation system(INS)by the traditional algorithm,such as the accumulated errors and the lack of observation of heading and altitude information,have become obstacles to the application and development of the PNS.In this paper,we introduce a heuristic heading constraint method.First of all,according to the movement characteristics of human gait,we use the generalized likelihood ratio test(GLRT)detector and introduce a time threshold to classify the human gait,so that we can effectively identify the stationary state of the foot.In addition,based on zero velocity update(ZUPT)and zero angular rate update(ZARU),the cumulative error of the inertial measurement unit(IMU)is limited and corrected,and then a heuristic heading estimation is used to constrain and correct the heading of the pedestrian.After simulation and experiments with low-cost IMU,the method is proved to reduce the localization error of end-point to less than 1%of the total distance,and it has great value in application.
基金supported by National Natural Science Foundation (Grant No.41475040) of Chinaproject from Shanghai Science and Technology Committee (Grant No.16dz1206100)
文摘Timely and relatively accurate detection of the center position of a near-landfalling typhoon is essential for weather forecasts, warning, disaster preparedness and data assimilation. Due to the vortex like nature of the flow near the typhoon center, the Zero Radial Velocity Line(ZRVL) of a Doppler radar extends through the typhoon center and the radar site. This paper proposes a method to detect the typhoon center by intersecting the ZRVLs of a dual-Doppler radar system. Simulation with Rankine Vortex model shows the validity of this method. This paper also evaluated the method by locating the centers of Typhoon FUNG-WONG(No. 201416) which impacted the east coastal regions of China from the afternoon of 22 September to the morning of 23 September, 2014 BST(Beijing Standard Time). During this period, the centers of the coastal typhoon were precisely positioned by intersecting the ZRVLs of two Doppler radars. The positioning accuracy of this method is comparable to that from Automatic Weather Stations(AWS) wind data. This method is suitable for areas where AWS data are unavailable, supposing radars are apart 100 km with a maximum unambiguous distance of 148 km, this technique can cover most regions effectively.
文摘This paper investigates the magnetic field component impact on cathode spots motion trajectory and the mechanism of periodic contraction.Electromagnetic coils and permanent magnets were installed at the different sides of cathode surface,the photographs of cathode spots motion trajectory were captured by a camera.Increasing the number of magnets and decreasing the distance between magnets and cathode both lead to enhancing cathode spots motion velocity.Radii of cathode spots trajectory decrease gradually with the increasing of electromagnetic coil＇s current,from 40 mm at 0 A to 10 mm at 2.7 A.Parallel magnetic field component intensity influence the speed of cathode spots rotate motion,and perpendicular magnetic field component drives spots drift in the radial direction.Cathode spot＇s radial drift is controlled by changing the location of the ‘zero line＇ where perpendicular magnetic component shifts direction and the radius of cathode spots trajectory almost equal to ‘zero line＇.
文摘In outdoor environments, GPS is often used for pedestrian navigation by utilizing its signals for position computation, but in indoor or semi-obstructed environments, GPS signals are often unavailable. Therefore, pedestrian navigation for these environments should be realized by the integration of GPS and inertial navigation system （INS）. However, the lowcost INS could induce errors that may result in a large position drift. The problem can be minimized by mounting the sensors on the pedestrian＇s foot, using zero velocity update （ZUPT） method with the standard navigation algorithm to restrict the error growth. However, heading drift still remains despite using ZUPT measurements since the heading error is unobservable. Also, tbot mounted INS suffers from the initialization ambiguity of position and heading from GPS. In this paper, a novel algorithm is developed to mitigate the heading drift problem when using ZUPT. The method uses building lay- out to aid the heading measurement in Kalman filter, and it could also be combined for the initial- ization. The algorithm has been investigated with real field trials using the low cost Microstrain 3DM-GX3-25 inertial sensor, a Leica GS10 GPS receiver and a uBlox EVK-6T GPS receiver. It could be concluded that the proposed method offers a significant improvement in position accuracy for the long period, allowing pedestrian navigation for nearly40 min with mean position error less than 2.8 m. This method also has a considerable effect on the accuracy of the initialization.
文摘Dead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change.Pedestrian Dead Reckoning(PDR)applies this concept to walking persons.The method can be used to track someone's movement in a building after a known landmark like the building's entrance is registered.Here,the movement of a foot and the corresponding direction change is measured and summed up,to infer the current position.Measuring and integrating the corresponding physical parameters,e.g.using inertial sensors,introduces small errors that accumulate quickly into large distance errors.Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths.In this paper,we use building maps to improve localization based on a single foot-mounted inertial sensor.We describe our localization method using zero velocity updates to accurately compute the length of individual steps and a Madgwick filter to determine the step orientation.Even though the computation of individual steps is quite accurate,small errors still accumulate in the long term.We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks,such as orientation and displacement drift,and discuss restrictions and disadvantages of these algorithms.We also present a method of deriving the initial position and orientation from GPS measurements.We verify our PDR correction methods analyzing the corrected and raw trajectories of six participants walking four routes of varying length and complexity through an office building,walking each route three times.Our quantitative results show an endpoint accuracy improvement of up to 60%when using likely paths and 23%when using unlikely paths.However,both approaches can also decrease accuracy in certain scenarios.We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods.
