Robust Multi-sensor Fusion via Factor Graph and Variational Bayesian Inference
Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022)Lecture Notes in Electrical Engineering(2023)
摘要
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.
更多查看译文
关键词
fusion,factor graph,multi-sensor
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要