用廉价单频接收机实现厘米级定位,无需基站
Empowering a Single-Frequency GNSS Receiver to Achieve High-Precision Positioning with Relative Observations

- 融合相对运动传感器与载波相位跟踪构建滑动窗口因子图
- 实测将定位精度从几米提升至分米级,跨环境稳定可靠
- 适合低成本自动驾驶、机器人等场景的高精度定位需求
全球导航卫星系统(GNSS)广泛用于野外机器人绝对定位。实时动态(RTK)技术可实现厘米级精度,但成本高且依赖额外基础设施。本文提出一种新型紧耦合状态估计框架,仅使用低成本单频GNSS接收机与任意相对运动传感器(如轮速计、相机、激光雷达),即可实现高精度定位。通过滑动窗口因子图融合通用相对运动信息与连续载波相位跟踪得到的全局历元-锚点约束。为摆脱对物理基准站的依赖,引入虚拟锚机制:首次观测卫星时即锁定其状态为虚拟参考,建立全局约束。通过单频多模态运动先验和鲁棒周跳恢复技术,替代多频硬件冗余,保障低成本接收机的载波相位完整性。在异构低成本传感器套件上的大量实测验证表明,该方法将单频接收机精度从数米提升至分米级,在多种环境下表现稳定,为自主导航提供了一种高精度、低成本、可靠的替代方案。
原文摘要 · Abstract (English)
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.
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