ABSTRACT
The covariance matrix in the current mainstream visual-inertial navigation system is artificially set and the weight of visual information cannot be adjusted by different blur degree, which cause the poor accuracy and robustness in the whole system. In order to solve this problem, this paper proposed a navigation scheme based on adaptive covariance matrix. This method used the Laplacian operator to evaluate the blur degree of image by a score. And then the visual covariance matrix is adjusted according to the different scores, which can adjust the weight in the fusion system according to the image quality. By doing this, the algorithm can improve the accuracy of the system. The simulation results show that the proposed method can effectively improve the system accuracy. Compared with the traditional method, the proposed algorithm has stronger robustness when motion blur occur.
- Durrant-Whyte, H., and Bailey, T. (2006). Simultaneous localization and mapping: part I. IEEE robotics & automation magazine, 13(2), 99-110.Google Scholar
- Engel, J., Schöps, T., and Cremers, D. (2014, September). LSD-SLAM: Large-scale direct monocular SLAM. In European conference on computer vision (pp. 834-849). Springer, Cham.Google Scholar
- Mur-Artal, R., Montiel, J. M. M., and Tardos, J. D. (2015). ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE transactions on robotics, 31(5), 1147-1163.Google ScholarDigital Library
- Engel, J., Koltun, V., and Cremers, D. (2017). Direct sparse odometry. IEEE transactions on pattern analysis and machine intelligence, 40(3), 611-625.Google Scholar
- Klein, G., and Murray, D. (2007, November). Parallel tracking and mapping for small AR workspaces. In 2007 6th IEEE and ACM international symposium on mixed and augmented reality (pp. 225-234). IEEE.Google Scholar
- Forster, C., Pizzoli, M., and Scaramuzza, D. (2014, May). SVO: Fast semi-direct monocular visual odometry. In 2014 IEEE international conference on robotics and automation (ICRA) (pp. 15-22). IEEE.Google Scholar
- Liu Zhenbin, Wei Shuangfeng, PANG Fan, 2020. Simultaneous localization and mapping scheme based on monocular and IMU. Science of Surveying and Mapping, 45(9): 86-95.Google Scholar
- Dezhi Wang. 2017. Research on mobile robot positioning based on ROS-based inertial navigation and visual information fusion. Harbin Institute of Technology, harbin, China.Google Scholar
- Jung, S. H., and Taylor, C. J. (2001, December). Camera trajectory estimation using inertial sensor measurements and structure from motion results. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 (Vol. 2, pp. II-II). IEEE.Google Scholar
- Weiss, S., Achtelik, M. W., Lynen, S., Chli, M., and Siegwart, R. (2012, May). Real-time onboard visual-inertial state estimation and self-calibration of mavs in unknown environments. In 2012 IEEE international conference on robotics and automation (pp. 957-964). IEEE.Google Scholar
- Lynen, S., Achtelik, M. W., Weiss, S., Chli, M., and Siegwart, R. (2013, November). A robust and modular multi-sensor fusion approach applied to mav navigation. In 2013 IEEE/RSJ international conference on intelligent robots and systems (pp. 3923-3929). IEEE.Google Scholar
- Mourikis, A. I., and Roumeliotis, S. I. (2007, April). A multi-state constraint Kalman filter for vision-aided inertial navigation. In Proceedings 2007 IEEE International Conference on Robotics and Automation (pp. 3565-3572). IEEE.Google Scholar
- Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J. J., and Dellaert, F. (2012). iSAM2: Incremental smoothing and mapping using the Bayes tree. The International Journal of Robotics Research, 31(2), 216-235.Google ScholarDigital Library
- Qi Wang. 2018. Research on Monocular VINS System Based on Nonlinear Optimization. Harbin Institute of Technology, harbin, China.Google Scholar
- Qin, T., Li, P., and Shen, S. (2018). Vins-mono: A robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics, 34(4), 1004-1020.Google ScholarDigital Library
- Qin, T., and Shen, S. (2017, September). Robust initialization of monocular visual-inertial estimation on aerial robots. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4225-4232). IEEE.Google Scholar
- Li, P., Qin, T., Hu, B., Zhu, F., and Shen, S. (2017, October). Monocular visual-inertial state estimation for mobile augmented reality. In 2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 11-21). IEEE.Google Scholar
- Qin, T., Li, P., and Shen, S. (2018, May). Relocalization, global optimization and map merging for monocular visual-inertial SLAM. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1197-1204). IEEE.Google Scholar
- Huang Yan-Ning, Li Wei-Hong, Cui Jin-Kai, Gong Wei-Guo. Strong edge extraction network for non-uniform blind motion image deblurring. Acta Automatica Sinica, 2021, 47(11): 2637−2653 doi: 10.16383/j.aas.c190654Google Scholar
- Burri, M., Nikolic, J., Gohl, P., Schneider, T., Rehder, J., Omari, S., ... and Siegwart, R. (2016). The EuRoC micro aerial vehicle datasets. The International Journal of Robotics Research, 35(10), 1157-1163.Google ScholarDigital Library
- Geiger, A., Lenz, P., Stiller, C., and Urtasun, R. (2013). Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11), 1231-1237.Google ScholarDigital Library
- Schubert, D., Goll, T., Demmel, N., Usenko, V., Stückler, J., and Cremers, D. (2018, October). The TUM VI benchmark for evaluating visual-inertial odometry. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1680-1687). IEEE.Google Scholar
- Blanco-Claraco, J. L., Moreno-Duenas, F. A., and González-Jiménez, J. (2014). The Málaga urban dataset: High-rate stereo and LiDAR in a realistic urban scenario. The International Journal of Robotics Research, 33(2), 207-214.Google ScholarDigital Library
- Cortés, S., Solin, A., Rahtu, E., and Kannala, J. (2018). ADVIO: An authentic dataset for visual-inertial odometry. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 419-434).Google ScholarCross Ref
- Grupp, M. (2017). evo: Python package for the evaluation of odometry and slam. Note: https://github. com/MichaelGrupp/evo Cited by: Table, 7.Google Scholar
- Dai Wenzheng, Y. Y. (2019). Noise image edge detection based on improved Gauss-Laplace operator. Application Research of Computers, 36(8), 2544-2547.Google Scholar
Recommendations
Least-Squares Covariance Matrix Adjustment
We consider the problem of finding the smallest adjustment to a given symmetric $n \times n$ matrix, as measured by the Euclidean or Frobenius norm, so that it satisfies some given linear equalities and inequalities, and in addition is positive ...
The rank of the covariance matrix of an evanescent field
Evanescent random fields arise as a component of the 2D Wold decomposition of homogeneous random fields. Besides their theoretical importance, evanescent random fields have a number of practical applications, such as in modeling the observed signal in ...
Comparison of linear shrinkage estimators of a large covariance matrix in normal and non-normal distributions
The problem of estimating the large covariance matrix of both normal and non-normal distributions is addressed. In convex combinations of the sample covariance matrix and a positive definite target matrix, the optimal weight is estimated by exact or ...
Comments