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This study focuses on the critical aspect of robust state estimation for thesafe navigation of an Autonomous Vehicle (AV). Existing literature primarilyemploys two prevalent techniques for state estimation, namely filtering-basedand graph-based approaches. Factor Graph (FG) is a graph-based approach,constructed using Values and Factors for Maximum Aposteriori (MAP) estimation,that offers a modular architecture that facilitates the integration of inputsfrom diverse sensors. However, most FG-based architectures in current userequire explicit knowledge of sensor parameters and are designed for singlesetups. To address these limitations, this research introduces a novelplug-and-play FG-based state estimator capable of operating without predefinedsensor parameters. This estimator is suitable for deployment in multiple sensorsetups, offering convenience and providing comprehensive state estimation at ahigh frequency, including mean and covariances. The proposed algorithmundergoes rigorous validation using various sensor setups on two differentvehicles: a quadricycle and a shuttle bus. The algorithm provides accurate androbust state estimation across diverse scenarios, even when faced with degradedGlobal Navigation Satellite System (GNSS) measurements or complete outages.These findings highlight the efficacy and reliability of the algorithm inreal-world AV applications.

Vehicle State Estimation through Modular Factor Graph-based Fusion of Multiple Sensors