This study focuses on the critical aspect of robust state estimation for the safe navigation of an Autonomous Vehicle (AV). Existing literature primarily employs two prevalent techniques for state estimation, namely filtering-based and 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 inputs from diverse sensors. However, most FG-based architectures in current use require explicit knowledge of sensor parameters and are designed for single setups. To address these limitations, this research introduces a novel plug-and-play FG-based state estimator capable of operating without predefined sensor parameters. This estimator is suitable for deployment in multiple sensor setups, offering convenience and providing comprehensive state estimation at a high frequency, including mean and covariances. The proposed algorithm undergoes rigorous validation using various sensor setups on two different vehicles: a quadricycle and a shuttle bus. The algorithm provides accurate and robust state estimation across diverse scenarios, even when faced with degraded Global Navigation Satellite System (GNSS) measurements or complete outages. These findings highlight the efficacy and reliability of the algorithm in real-world AV applications.

翻译：本研究聚焦于自主车辆（AV）安全导航中关键环节——鲁棒状态估计。现有文献主要采用两种主流状态估计技术：滤波法与图解法。因子图（FG）作为图解法的一种，通过值和因子实现最大后验（MAP）估计，其模块化架构便于融合来自不同传感器的输入数据。然而，当前大多数基于FG的架构要求明确已知传感器参数，且仅针对单一传感器配置设计。为解决上述局限，本研究提出一种新型即插即用型FG状态估计器，可在无需预设传感器参数的情况下运行。该估计器适用于多种传感器配置，具备便捷性，并能以高频率提供包含均值和协方差在内的完整状态估计。所提算法在两种不同车辆（四轮车和摆渡巴士）上通过多种传感器配置进行了严格验证。结果表明，即使在全球导航卫星系统（GNSS）测量降级或完全中断的情况下，该算法仍能在多种场景下提供精确且鲁棒的状态估计。这些发现凸显了该算法在实际AV应用中的有效性和可靠性。