Factor Graph Optimization for Real-Time Positioning in Unmanned Driving

Global navigation satellite systems (GNSS) have made significant advancements in recent years, allowing for free access and the provision of more dependable services. However, the conventional method produces erroneous positions and has to be modified. This paper proposes a factor graph optimization for real-time positioning in unmanned driving. GNSS/INS provides precise measurements of position data with better accuracy and robustness, and the use of the iSAM algorithm minimizes costs while enhancing the accuracy of the algorithm. Through field tests, compared with the data that had been filtered using the Kalman filtering, the outcomes demonstrated that our real-time localization and algorithmic findings performed better than the Kalman filter fusion results in precision.