融合AR姿态与惯导GPS，提升城市环境下单目视觉里程计精度。
Enhanced Monocular Visual Odometry with AR Poses and Integrated INS-GPS for Robust Localization in Urban Environments
- 用AR姿态解决单目里程计尺度模糊问题，融合惯导与GPS数据
- 在1公里轨迹上实现1.529米的均方根误差，达到车道级定位精度
- 仅需低成本硬件，适合移动设备实时部署，适合自动驾驶导航
本文提出一种成本低廉的定位系统，结合单目视觉里程计、增强现实（AR）姿态及集成惯性导航系统（INS）-GPS数据。通过AR姿态解决单目视觉里程计的尺度不确定性，并利用惯导与GPS数据进一步提升精度，所有数据经由扩展卡尔曼滤波器（Extended Kalman Filter）融合。实验基于谷歌街景手动标注轨迹，在1公里测试路径上取得1.529米的均方根误差（RMSE）。未来工作将聚焦于实时移动端实现，以及进一步融合视觉-惯性里程计以增强鲁棒性。该方法在最小硬件需求下实现车道级精度，使高精度导航更易普及。
原文摘要 · Abstract (English)
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.
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