[1] GUI J, GU D, SEN W, et al. A review of visual inertial odometry from filtering and optimization perspectives[J]. Advanced
[2] SAPUTRA M R U, MARKHAM A, TRIGONI N. Visual SLAM and structure from motion in dynamic environments: a survey[J].
[3] BABU B P W,CYGANSKI D,DUCKWORTH J,et al. Detection and resolution of motion conflict in visual inertial odometry[C]//
[4] QIN T, LI P, SHEN S. VINS-mono: a robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on
[5] ZHU T, MA H. Challenges of dynamic environment for visual-inertial odometry[C]// Proceedings of the 3rd International
[6] MOURIKIS A I,ROUMELIOTIS S I. A multi-state constraint Kalman filter for vision-aided inertial navigation[C]// Proceedings
[7] LEUTENEGGER S, LYNEN S, BOSSE M, et al. Keyframe-based visual-inertial odometry using nonlinear optimization[J].
[8] MUR-ARTAL R, TARDóS J D. Visual-inertial monocular SLAM with map reuse[J]. IEEE Robotics & Automation Letters,
[9] BAHRAINI M S,BOZORG M,RAD A B. SLAM in dynamic environments via ML-RANSAC[J]. Mechatronics, 2018(49):105-118.
[10] BURRI M, NIKOLIC J, GOHL P, et al. The EuRoC micro aerial vehicle datasets[J]. The International Journal of Robotics
[11] OVRéN H, FORSSéN P. Spline error weighting for robust visual-inertial fusion[C]// Proceedings of the 2018 IEEE/CVF
[12] SCHUBERT D,GOLL T,DEMMEL N,et al. The TUM VI benchmark for evaluating visual-inertial odometry[C]// Proceedings of
[13] CORTéS S, SOLIN A, RAHTU E, et al. ADVIO: an authentic dataset for visual-inertial odometry[C]// Proceedings of the
[14] STURM J,ENGELHARD N,ENDRES F,et al. A benchmark for the evaluation of RGB-D SLAM systems[C]// Proceedings of the