用单目视觉+3D地图实现无卫星信号环境下的精准定位
Look to Locate: Vision-Based Multisensory Navigation with 3-D Digital Maps for GNSS-Challenged Environments
- 结合单目深度估计与语义过滤，通过3D地图注册提升定位精度
- 室内定位误差小于1米占比达92%，室外超过80%，水平误差0.98米
- 适合自动驾驶、无人机等在无GPS区域运行的场景
在无全球导航卫星系统（GNSS）信号的环境（如地下停车场或密集城市峡谷）中，实现高精度且鲁棒的车辆定位仍具挑战。本文提出一种低成本、基于视觉的多传感器导航系统，融合单目深度估计、语义过滤与视觉地图注册（VMR），并利用3D数字地图进行定位。在真实室内外驾驶场景中大量测试表明，该系统在室内实现92%的定位精度优于1米，在室外超过80%，水平定位与航向的平均均方根误差分别为约0.98米和1.25度。相比基线方法，本方案显著减少漂移，在各种条件下平均定位精度提升约88%。该工作展示了低成本单目视觉系统结合3D地图在陆上车辆中实现可扩展、非依赖GNSS导航的巨大潜力。
原文摘要 · Abstract (English)
In Global Navigation Satellite System (GNSS)-denied environments such as indoor parking structures or dense urban canyons, achieving accurate and robust vehicle positioning remains a significant challenge. This paper proposes a cost-effective, vision-based multi-sensor navigation system that integrates monocular depth estimation, semantic filtering, and visual map registration (VMR) with 3-D digital maps. Extensive testing in real-world indoor and outdoor driving scenarios demonstrates the effectiveness of the proposed system, achieving sub-meter accuracy of 92% indoors and more than 80% outdoors, with consistent horizontal positioning and heading average root mean-square errors of approximately 0.98 m and 1.25 °, respectively. Compared to the baselines examined, the proposed solution significantly reduced drift and improved robustness under various conditions, achieving positioning accuracy improvements of approximately 88% on average. This work highlights the potential of cost-effective monocular vision systems combined with 3D maps for scalable, GNSS-independent navigation in land vehicles.
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