Abstract
In the research, we present a novel nonlinear state estimation method to solve the problems of multi-sensor fusion application in navigation systems. In a full Bayesian framework, the muti-sensor fusion is performed by estimating the maximum a posterior over the joint probability distribution function (PDF) of all state variables. In order to exploit the full sparsity of the system, the joint PDF is represented by a factor graph model. Since the outliers which represent corrupted observations could affect the accuracy of state estimation, a variational approximation scheme is applied for robust multi-sensor fusion. The proposed method is experimentally verified using the multi-sensor data that recoded by an integrated navigation system. The simulation results demonstrate that the proposed method provides a comparable performance to the traditional muti-sensor fusion method.
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Zhou, Y., Mei, C., Liu, T., Bai, L. (2023). Robust Multi-sensor Fusion via Factor Graph and Variational Bayesian Inference. In: Fu, W., Gu, M., Niu, Y. (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). ICAUS 2022. Lecture Notes in Electrical Engineering, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-99-0479-2_2
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DOI: https://doi.org/10.1007/978-981-99-0479-2_2
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