A new two-iteration sculling compensation mathematical framework is provided for modern-day strapdown inertial navigation system（SINS） algorithm design that utilizes a new concept in velocity updating. The principal...A new two-iteration sculling compensation mathematical framework is provided for modern-day strapdown inertial navigation system（SINS） algorithm design that utilizes a new concept in velocity updating. The principal structure of this framework includes twice sculling compensation procedure using incremental outputs from the inertial system sensors during the velocity updating interval. Then, the moderate algorithm is designed to update the velocity parameter. The analysis is conducted in the condition of sculling motion which indicates that the new mathematical framework error which is smaller than the conventional ones by at least two orders is far superior. Therefore, a summary is given for SINS software which can be designed with the new mathematical framework in velocity updating.展开更多
Stacking velocity V_（C2）,vertical velocity ratio γ_0,effective velocity ratio γ_（eff）,and anisotropic parameter x_（eff） are correlated in the PS-converted-wave（PS-wave） anisotropic prestack Kirchhoff time mi...Stacking velocity V_（C2）,vertical velocity ratio γ_0,effective velocity ratio γ_（eff）,and anisotropic parameter x_（eff） are correlated in the PS-converted-wave（PS-wave） anisotropic prestack Kirchhoff time migration（PKTM） velocity model and are thus difficult to independently determine.We extended the simplified two-parameter（stacking velocity V_（C2） and anisotropic parameter k_（eff）） moveout equation from stacking velocity analysis to PKTM velocity model updating and formed a new four-parameter（stacking velocity V_（C2）,vertical velocity ratio γ_0,effective velocity ratio γ_（eff）,and anisotropic parameter k_（eff）） PS-wave anisotropic PKTM velocity model updating and process flow based on the simplified twoparameter moveout equation.In the proposed method,first,the PS-wave two-parameter stacking velocity is analyzed to obtain the anisotropic PKTM initial velocity and anisotropic parameters;then,the velocity and anisotropic parameters are corrected by analyzing the residual moveout on common imaging point gathers after prestack time migration.The vertical velocity ratio γ_0 of the prestack time migration velocity model is obtained with an appropriate method utilizing the P- and PS-wave stacked sections after level calibration.The initial effective velocity ratio γ_（eff） is calculated using the Thomsen（1999） equation in combination with the P-wave velocity analysis;ultimately,the final velocity model of the effective velocity ratio γ_（eff） is obtained by percentage scanning migration.This method simplifies the PS-wave parameter estimation in high-quality imaging,reduces the uncertainty of multiparameter estimations,and obtains good imaging results in practice.展开更多
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
基金supported by the National Natural Science Foundation of China(90816027)the Aviation Science Funds(20135853037)+1 种基金the Foundation of China Aerospace Science & Industry Corporation(2013HTXGD2014HTXGD)
文摘A new two-iteration sculling compensation mathematical framework is provided for modern-day strapdown inertial navigation system（SINS） algorithm design that utilizes a new concept in velocity updating. The principal structure of this framework includes twice sculling compensation procedure using incremental outputs from the inertial system sensors during the velocity updating interval. Then, the moderate algorithm is designed to update the velocity parameter. The analysis is conducted in the condition of sculling motion which indicates that the new mathematical framework error which is smaller than the conventional ones by at least two orders is far superior. Therefore, a summary is given for SINS software which can be designed with the new mathematical framework in velocity updating.
基金supported by the Important National Science&Technology Specific Projects(No.2011ZX05019-003)the New Method and Technology Research Project of Geophysical Exploration of CNPC(No.2014A-3612)
文摘Stacking velocity V_（C2）,vertical velocity ratio γ_0,effective velocity ratio γ_（eff）,and anisotropic parameter x_（eff） are correlated in the PS-converted-wave（PS-wave） anisotropic prestack Kirchhoff time migration（PKTM） velocity model and are thus difficult to independently determine.We extended the simplified two-parameter（stacking velocity V_（C2） and anisotropic parameter k_（eff）） moveout equation from stacking velocity analysis to PKTM velocity model updating and formed a new four-parameter（stacking velocity V_（C2）,vertical velocity ratio γ_0,effective velocity ratio γ_（eff）,and anisotropic parameter k_（eff）） PS-wave anisotropic PKTM velocity model updating and process flow based on the simplified twoparameter moveout equation.In the proposed method,first,the PS-wave two-parameter stacking velocity is analyzed to obtain the anisotropic PKTM initial velocity and anisotropic parameters;then,the velocity and anisotropic parameters are corrected by analyzing the residual moveout on common imaging point gathers after prestack time migration.The vertical velocity ratio γ_0 of the prestack time migration velocity model is obtained with an appropriate method utilizing the P- and PS-wave stacked sections after level calibration.The initial effective velocity ratio γ_（eff） is calculated using the Thomsen（1999） equation in combination with the P-wave velocity analysis;ultimately,the final velocity model of the effective velocity ratio γ_（eff） is obtained by percentage scanning migration.This method simplifies the PS-wave parameter estimation in high-quality imaging,reduces the uncertainty of multiparameter estimations,and obtains good imaging results in practice.
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.
