Abstract
In this paper, a robust monocular visual-inertial SLAM based on nonlinear optimization is proposed. In our method, visual feature points are assigned different information matrices according to the image pyramid layers at which the features are extracted. IMU pre-integration strategy is adopted to avoid repeated IMU integration caused by initial states change in optimization. Meanwhile, we adopted the strategies of sliding window and marginalization in order to yield higher precision of states estimation and restrict the computational complexity. Experiments are designed to compare our algorithm with MSCKF and VINS on EuRoC dataset, and the results show that our method can effectively estimate the motion and sparse map.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant No. 41874034), the National Science and Technology Major Project of the National Key R&D Program of China (Grant No. 2016YFB0502102), the Beijing Natural Science Foundation (Grant No. 4202041), the Aeronautical Science Foundation of China.
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Duo, J., Ji, L., Zhao, L. (2021). Robust Monocular Visual-Inertial SLAM Using Nonlinear Optimization. In: Jia, Y., Zhang, W., Fu, Y. (eds) Proceedings of 2020 Chinese Intelligent Systems Conference. CISC 2020. Lecture Notes in Electrical Engineering, vol 705. Springer, Singapore. https://doi.org/10.1007/978-981-15-8450-3_59
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DOI: https://doi.org/10.1007/978-981-15-8450-3_59
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