This paper introduces a cost effective localization system combining monocular visual odometry , augmented reality (AR) poses, and integrated INS-GPS data. We address monocular VO scale factor issues using AR poses and enhance accuracy with INS and GPS data, filtered through an Extended Kalman Filter . Our approach, tested using manually annotated trajectories from Google Street View, achieves an RMSE of 1.529 meters over a 1 km track. Future work will focus on real-time mobile implementation and further integration of visual-inertial odometry for robust localization. This method offers lane-level accuracy with minimal hardware, making advanced navigation more accessible.

翻译：本文提出一种结合单目视觉里程计、增强现实（AR）位姿与集成INS-GPS数据的低成本定位系统。我们利用AR位姿解决单目视觉里程计的尺度因子问题，并通过扩展卡尔曼滤波器融合INS与GPS数据以提升精度。该方法在基于Google街景人工标注轨迹的测试中，于1公里路径上实现了1.529米的均方根误差。未来工作将聚焦于实时移动端部署及视觉-惯性里程计的深度集成，以增强定位鲁棒性。本方法以最小硬件成本实现车道级精度，为先进导航技术的普及提供了可行方案。