Accurate navigation is essential for autonomous robots and vehicles. In recent years, the integration of the Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and camera has garnered considerable attention due to its robustness and high accuracy in diverse environments. In such systems, fully utilizing the role of GNSS is cumbersome because of the diverse choices of formulations, error models, satellite constellations, signal frequencies, and service types, which lead to different precision, robustness, and usage dependencies. To clarify the capacity of GNSS algorithms and accelerate the development efficiency of employing GNSS in multi-sensor fusion algorithms, we open source the GNSS/INS/Camera Integration Library (GICI-LIB), together with detailed documentation and a comprehensive land vehicle dataset. A factor graph optimization-based multi-sensor fusion framework is established, which combines almost all GNSS measurement error sources by fully considering temporal and spatial correlations between measurements. The graph structure is designed for flexibility, making it easy to form any kind of integration algorithm. For illustration, four Real-Time Kinematic (RTK)-based algorithms from GICI-LIB are evaluated using our dataset. Results confirm the potential of the GICI system to provide continuous precise navigation solutions in a wide spectrum of urban environments.

翻译：精确导航对于自主机器人和车辆至关重要。近年来，全球导航卫星系统（GNSS）、惯性导航系统（INS）与相机的集成因其在不同环境中的鲁棒性和高精度而备受关注。在此类系统中，由于公式、误差模型、卫星星座、信号频率和服务类型的多样化选择，导致精度、鲁棒性和使用依赖性各异，从而使得充分利用GNSS的作用变得复杂。为阐明GNSS算法的能力，并提高在多传感器融合算法中运用GNSS的开发效率，我们开源了GNSS/INS/相机集成库（GICI-LIB），同时提供详细文档和一份全面的地面车辆数据集。该库基于因子图优化的多传感器融合框架，通过充分考虑测量值之间的时空相关性，几乎整合了所有GNSS测量误差源。图结构设计灵活，易于形成任意类型的集成算法。为进行说明，我们使用数据集评估了GICI-LIB中四种基于实时动态差分（RTK）的算法。结果证实了GICI系统在城市环境广泛场景中提供连续精确导航解决方案的潜力。