[3] GUO X T, SUN C K, WANG P, et al.Vision sensor and dual MEMS gyroscope integrated system for attitude determination on moving base [J].Review of Scientific Instruments, 2018, 89(1).doi:10.1063/1.5011703.
[4] FENG Y F, ZHANG Z Z, ZHAO X B, et al.GVCNN:group-view convolutional neural networks for 3D shape recognition [C]//IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City, UT:IEEE, 2018:264-272.
[5] WEI X, YU R X, SUN J.View-GCN:view-based graph convolutional network for 3D shape analysis[C]//IEEE Conference on Computer Vision and Pattern Recognition.Seattle, WA:IEEE, 2020:1847-1856.
[6] MATURANA D, SCHERER S.VoxNet:a 3D convolutional neural network for real-time object recognition [C]//IEEE International Conference on Intelligent Robots and Systems.Hamburg:IEEE, 2015:922-928.
[7] ZHANG Y, SONG K, YI J, et al.Pose estimation of a rigid body and its supporting moving platform using two gyroscopes and relative complementary measurements[C]//International Con- ference on Intelligent Robots and Systems.Daejeon:IEEE, 2016:90-95.
[8] PAIGWAR A, ERKENT O, WOLF C, et al.Attentional PointNet for 3D-object detection in point clouds[C]//IEEE Conference on Computer Vision and Pattern Recognition Workshops.Long Beach, CA:IEEE, 2019:1297-1306.
[9] LOBO J, DIAS J M.Relative pose calibration between visual and inertial sensors[J].The International Journal of Robotics Research, 2007, 26(6):561-575.
[10] LEUTENEGGER S, LYNEN S, BOSSE M, et al.Keyframe-based visual-inertial odometry using nonlinear optimization[J].The International Journal of Robotics Research, 2015, 34(3):314-334.
[11] MIRZAEI F M, ROUMELIOTIS S I.1|A Kalman filter-based algorithm for IMU-camera calibration[C]//IEEE International Conference on Intelligent Robots and Systems.San Diego, CA:IEEE, 2007:2427-2434.
[12] LI M Y, YU H S, ZHENG X, et al.High-fidelity sensor modeling and self-calibration in vision-aided inertial navigation[C]//IEEE International Conference on Robotics and Automation.Hong Kong:IEEE, 2014: 409-416.
[15] SOL J.Quaternion kinematics for the error-state Kalman filter[R].Barcelona:IRI, 2016.
[16] MEHRA R K.Approaches to adaptive filtering [J].IEEE Transactions on Automatic Control, 1972, 17(5):693-698.
[17] KELLY J S, SUKHATME G S.Visual-inertial sensor fusion:localization, mapping and sensor-to-sensor self-calibration[J].The International Journal of Robotics Research, 2011, 30(1):56-79.
[18] RANDENIYA D I B, GUNARATNE M, SARKAR S, et al.Calibration of inertial and vision systems as a prelude to multi-sensor fusion[J].Transportation Research, Part C:Emerging Technologies, 2008, 16(2): 255-274.
[19] REHDER J, SIEGWART R, FURGALE P.A general approach to spatiotemporal calibration in multisensor systems[J].IEEE Transactions on Robotics, 2016, 32(2):383-398.
[20] HUANG W B, LIU H.Online initialization and automatic camera-IMU extrinsic calibration for monocular visual-inertial SLAM[C]//IEEE International Conference on Robotics & Automation.Brisbane, QLD:IEEE, 2018:5182-5189.
[21] QIN T, LI P L, SHEN S J.VINS-Mono:a robust and versatile monocular visual-inertial state estimator[J].IEEE Transactions on Robotics, 2018, 34(4):1004-1020.
[22] KIM K S, JANG D S, CHOI H I.Real time face tracking with pyramidal Lucas-Kanade feature tracker[C]//International Conference on Computational Science and Its Applications.Kuala Lumpur:Springer, 2007:1074-1082.
[23] HEO S J, SHIN O S, PARK C G.Motion and structure estimation using fusion of inertial and vision data for helmet tracker[J].International Journal of Aeronautical and Space Sciences, 2010, 11(1):31-40.
[24] ARASARATNAM I, HAYKIN S.Cubature Kalman filters[J].IEEE Transactions on Automatic Control, 2009, 54(6):1254-1269.
[25] DUNIK J, STRAKA O, KOST O, et al.Noise covariance matrices in state-space models:a survey and comparison of estimation methods - Part I[J].International Journal of Adaptive Control and Signal Processing, 2017, 31(11):1505-1543.
[26] DAVARI N, GHOLAMI A.Variational Bayesian adaptive Kalman filter for asynchronous multirate multi-sensor integrated navigation system [J].Ocean Engineering, 2019, 174:108-116.
[27] KARASALO M, HU X M.An optimization approach to adaptive Kalman filtering[J].Automatica, 2011, 47(8):1785-1793.
[28] GAO X D, YOU D Y, KATAYAMA S.Seam tracking monitoring based on adaptive Kalman filter embedded Elman neural network during high-power fiber laser welding[J].IEEE Transactions on Industrial Electronics, 2012, 59(11):4315-4325.
[29] HUANG Y L, ZHANG Y G, XU B, et al.A new adaptive extended Kalman filter for cooperative localization[J].IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(1):353-368.
[30] SARKKA S, NUMMENMAA A.Recursive noise adaptive Kalman filtering by variational Bayesian approximations[J].IEEE Transactions on Automatic Control, 2009, 54(3):596-600.
[31] MA J R, LAN H, WANG Z F, et al.Improved adaptive Kalman filter with unknown process noise covariance[C]//The 21st International Conference on Information Fusion.Cambridge:IEEE, 2018:1-5.
[32] BAI M M, HUANG Y L, ZHANG Y G, et al.A novel progressive Gaussian approximate filter for tightly coupled GNSS/INS integration[J].IEEE Transactions on Instrumenta-tion and Measurement, 2020, 69:3493-3505.
[33] HE J J, SUN C K, ZHANG B S, et al.Adaptive error-state Kalman filter for attitude determination on a moving platform [J].IEEE Tran-sactions on Instrumentation and Measurement, 2021, 70:1-10.
[34] KARLGAARD C D, SCHAUB H.Comparison of several nonlinear filters for a benchmark tracking problem[C]//AIAA Guidance, Navigation, and Control Conference and Exhibit.Keystone:AIAA, 2006. doi:10.2514/6.2006-6243.
[35] GUSTAFSSON F.Particle filter theory and practice with positioning applications[J].IEEE Aerospace and Electronic Systems Magazine, 2010, 25(7):53-82.
[36] HUANG Y L, ZHANG Y G, LI N, et al.A novel robust students t-based Kalman filter[J].IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(3):1545-1554.
[37] KARLGAARD C D, SCHAUB H.Adaptive nonlinear Huber-based navigation for rendezvous in elliptical orbit[J].Journal of Guidance, Control, and Dynamics, 2011, 34(2):388-402.
[39] CHEN B D, DANG L J, GU Y T, et al.Minimum error entropy Kalman filter[J].IEEE Transactions on Systems, Man, and Cybernetics:Sys-tems, 2021, 51(9):5819-5829.
[40] LIU X, QU H, ZHAO J H, et al.Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems[J].ISA Transactions, 2018, 80:195-202.
[41] CHEN B D, LIU X, ZHAO H Q, et al.Maximum correntropy Kalman filter[J].Automatica, 2017, 76:70- 77.
[43] HE J J, SUN C K, ZHANG B S, et al.Variational Bayesian-based maximum correntropy cubature Kalman filter with both adaptivity and robustness[J].IEEE Sensors Journal, 2021, 21(2):1982-1992.
[44] ZHOU S L, FEI F, ZHANG G L, et al.2D human gesture tracking and recognition by the fusion of MEMS inertial and vision sensors[J].IEEE Sensors Journal, 2014, 14(4): 1160-1170.
[45] WANG Y F, NGUYEN B M, FUJIMOTO H, et al.Multirate estimation and control of body slip angle for electric vehicles based on onboard vision system[J].IEEE Transactions on Industrial Electronics, 2014, 61(2):1133-1143.
[46] ARMESTO L, TORNERO J, VINCZE M.Fast ego-motion estimation with multi-rate fusion of inertial and vision[J].The International Journal of Robotics Research, 2007, 26(6):577-589.
[47] GUO X T, SUN C K, WANG P.Multi-rate cubature Kalman filter based data fusion method with residual compensation to adapt to sampling rate discrepancy in attitude measurement system[J].Review of Scientific Instruments, 2017, 88(8).doi:10.1063/1.4997072.
[49] NGUYEN T, MANN G K I, VARDY A, et al.Developing a cubature multi-state constraint Kalman filter for visualinertial navigation system[C]//The 14th Conference on Computer and Robot Vision.Edmonton:IEEE, 2017:321-328.