用道路相似性匹配车载俯视图与卫星图，实现无导航定位
Road Similarity-Based BEV-Satellite Image Matching for UGV Localization
- 构建道路相似空间，融合激光与图像生成车载俯视图
- 10公里测试平均横向误差0.89米，平面误差3.41米
- 夜间也能稳定定位，适合复杂野外环境
为解决在无卫星信号的非公路环境中无人地面车辆（UGV）的自主定位难题，本研究提出一种基于匹配的定位方法，利用鸟瞰视角（BEV）感知图像与卫星地图在道路相似性空间中的对应关系，实现高精度定位。首先构建鲁棒的激光惯性里程计系统，并融合激光与图像数据生成车辆局部的BEV感知图像，有效缓解了地面视角图像与卫星地图之间的显著视角差异。随后将BEV图像与卫星地图投影至道路相似性空间，通过归一化互相关（NCC）计算匹配得分。最终采用粒子滤波估计车辆位姿的概率分布。与GNSS真值对比，该定位系统在长达10公里的测试中表现出稳定性，未出现发散，平均横向误差仅为0.89米，平均平面欧氏误差为3.41米。此外，系统在夜间条件下仍保持精准稳定的全局定位能力，进一步验证了其鲁棒性与适应性。
原文摘要 · Abstract (English)
To address the challenge of autonomous UGV localization in GNSS-denied off-road environments,this study proposes a matching-based localization method that leverages BEV perception image and satellite map within a road similarity space to achieve high-precision positioning.We first implement a robust LiDAR-inertial odometry system, followed by the fusion of LiDAR and image data to generate a local BEV perception image of the UGV. This approach mitigates the significant viewpoint discrepancy between ground-view images and satellite map. The BEV image and satellite map are then projected into the road similarity space, where normalized cross correlation (NCC) is computed to assess the matching score.Finally, a particle filter is employed to estimate the probability distribution of the vehicle's pose.By comparing with GNSS ground truth, our localization system demonstrated stability without divergence over a long-distance test of 10 km, achieving an average lateral error of only 0.89 meters and an average planar Euclidean error of 3.41 meters. Furthermore, it maintained accurate and stable global localization even under nighttime conditions, further validating its robustness and adaptability.
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