Factor Graph Localization Algorithm Based on Robust Estimation

Abstract

In recent years, with the continuous development of unmanned ground vehicles (UGVs), the performance requirements of unmanned vehicle positioning system are also gradually improved. Existing unmanned ground vehicles need to use a variety of sensors to provide measurement information, and the available positioning information is dynamically combined in the positioning system and the positioning results are calculated. In this paper, multi-source information fusion positioning system of unmanned ground vehicles is established based on factor graph model. In order to solve the problem that the factor graph algorithm is difficult to identify and exclude abnormal observations autonomously, this paper combines the robust estimation with the factor graph to improve the factor graph method. The reliability factor is added in the construction of the factor node, which can be adjusted adaptively according to the current measured value to reduce the weight of abnormal observations and ensure the normal operation of the system. Simulation results show that the improved factor graph algorithm proposed in this paper can effectively reduce the influence of abnormal observations on positioning accuracy.