Zeng Qinghua, Chen Weina, Liu Jianye, Wang Huizhe
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China.
Satellite Communication and Navigation Collaborative Innovation Center, Nanjing 211100, China.
Sensors (Basel). 2017 Mar 21;17(3):641. doi: 10.3390/s17030641.
An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different sensors efficiently is an important problem. The fact that different sensors provide measurements asynchronously may complicate the processing of these measurements. In addition, the output signals of some sensors appear to have a non-linear character. In order to incorporate these measurements and calculate a navigation solution in real time, the multi-sensor fusion algorithm based on factor graph is proposed. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the graph without considering the relationship between the sensor update frequency and the fusion period. An experimental MUAV system has been built and some experiments have been performed to prove the effectiveness of the proposed method.
集成导航系统与额外的传感器相结合可用于微型无人机（MUAV）应用，因为多传感器信息具有冗余性和互补性，这可以显著提高系统精度。如何有效处理从不同传感器收集的信息是一个重要问题。不同传感器异步提供测量数据这一事实可能会使这些测量数据的处理变得复杂。此外，一些传感器的输出信号似乎具有非线性特征。为了合并这些测量数据并实时计算导航解决方案，提出了基于因子图的多传感器融合算法。全局最优解根据因子图的链式结构进行分解，这允许条件概率密度采用更通用的形式。它可以将融合问题转化为将由这些测量定义的因子连接到图中，而无需考虑传感器更新频率与融合周期之间的关系。已经构建了一个实验性的微型无人机系统，并进行了一些实验来证明所提方法的有效性。
