利用相对观测实现单频GNSS接收机的高精度定位
Empowering a Single-Frequency GNSS Receiver to Achieve High-Precision Positioning with Relative Observations
AI总结：
提出一种紧耦合状态估计框架，通过单频GNSS接收机与相对运动传感器融合，利用滑动窗口因子图和虚拟锚点机制实现分米级定位，无需基站。
AI中文摘要：
全球导航卫星系统（GNSS）导航广泛应用于野外机器人领域，提供绝对、室外的定位。实时动态（RTK）技术的进步可实现厘米级精度，促进自主导航任务。然而，RTK的成本和额外基础设施仍阻碍其应用，需要更具成本效益的解决方案。本文提出一种新颖的紧耦合状态估计框架，通过使用低成本、大众市场的单频GNSS接收机与任意相对运动传感器（如轮式编码器、相机、激光雷达）实现高精度定位。我们提出一种滑动窗口因子图，将通用相对运动与源自连续载波相位跟踪的全局历元-锚点约束相结合。为消除对物理基站的依赖，我们引入虚拟锚点机制：在首次观测到卫星时，将其状态锁定为虚拟参考，以建立全局历元-锚点约束。通过用单频多模态运动先验和鲁棒的周跳恢复技术替代多频硬件冗余，我们的方法确保了廉价接收机上的载波相位完整性。在异构低成本传感器套件上进行的大量实际实验验证了我们的方法将单频接收机的精度从几米提高到分米级，在不同环境下为自主导航提供了准确、经济且可靠的替代方案。
英文摘要：
Global Navigation Satellite System (GNSS) navigation is widely used to provide absolute, outdoor positioning in field robotics. Advances in Real-Time Kinematic (RTK) technology can achieve centimeter-level accuracy, facilitating autonomous navigation tasks. However, the cost and extra infrastructure used for RTK still hinder the application and more cost-effective solutions are desired. In this letter, we present a novel tightly-coupled state estimation framework that achieves high-precision localization by using low-cost, mass-market single-frequency GNSS receivers with any relative motion sensors (e.g., wheel encoder, camera, LiDAR). We propose a sliding-window factor graph that integrates generic relative motion with global epoch-to-anchor constraints derived from continuous carrier phase tracking. To eliminate the reliance on physical base stations, we introduce a virtual anchor mechanism: upon the initial observation of a satellite, its state is locked as a virtual reference to establish global epoch-to-anchor constraints. By substituting multi-frequency hardware redundancy with single-frequency multi-modal kinematic priors and a robust cycle-slip recovery technique, our approach ensures carrier-phase integrity on cheap receivers. Extensive real-world experiments on heterogeneous low-cost sensor suites validate that our method improves the accuracy of a single-frequency receiver from several meters to decimeter-level precision across diverse environments, providing an accurate, cost-effective and reliable alternative for autonomous navigation.
