Abstract
A global navigation satellite system and inertial navigation system‐ (GNSS/INS‐) integrated system is employed to provide direct georeferencing (DG) in aerial photogrammetry. However, GNSS/INS suffers from stochastic error, strong nonlinearity, and weak observability problems in high dynamic or less maneuver scenarios. In this paper, we proposed a new triple filtering algorithm for aerial GNSS/INS integration. The new algorithm implements filtering in the sequence of forward, backward, and forward directions. Each filter is initialized by a previous filter to get a quick convergence, and the final result is combination of the last two filtering to smooth error. The proposed triple filtering strategy avoids inaccuracy in the 1st forward filtering when the system has not reached convergence. Moreover, it facilitates engineering implementation because backward filtering can employ the same equations with forward filtering. To assess stochastic error of the inertial measurement unit, the Allan variance method is used and abbreviated stochastic model is built. A real aerial testing is conducted, and the result indicates that DG can achieve horizontal accuracy of 5 cm by the proposed algorithm, which has 63% improvement compared to standard extended Kalman filter.
Highlights
Aerial photogrammetry performs surveying and mapping by taking images of ground from an elevated position
The images could be collected by sensors like digital cameras, Lidar, and multispectral or hyperspectral scanners, to make digital orthophoto map (DOM), digital elevation model (DEM), digital line graphic (DLG), and other mapping products
This paper studies the stochastic process modelling and smoothing method of GNSS/INS-integrated direct georeferencing system
Summary
Aerial photogrammetry performs surveying and mapping by taking images of ground from an elevated position. The primary error sources of GNSS/INS integration in aerial application are sensor stochastic error, strong nonlinearity, and weak observability, among which sensors’ stochastic errors are very intractable as many types of stochastic processes are involved. They cause accumulative positioning and orientation error with time. Classical extended Kalman filter (EKF) takes linearization to the nonlinear model and implements the linear KF algorithm for estimation of the state vector; it has been extensively used in many engineering applications It suffers from strong nonlinearity, such as large maneuver or environment influence.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
