基于道路相似性的BEV-卫星图像匹配用于UGV定位
Road Similarity-Based BEV-Satellite Image Matching for UGV Localization
AI总结：
本研究提出一种基于道路相似性的BEV-卫星图像匹配方法，用于GNSS缺失越野环境中UGV的高精度定位，通过LiDAR-惯性里程计和图像融合生成BEV图像，结合NCC匹配与粒子滤波，在10公里测试中实现平均横向误差0.89米和平面误差3.41米，并验证了夜间鲁棒性。
摘要：
为解决GNSS缺失的越野环境中自主UGV定位的挑战，本研究提出一种基于匹配的定位方法，利用BEV感知图像和卫星地图在道路相似性空间中实现高精度定位。我们首先实现鲁棒的LiDAR-惯性里程计系统，随后融合LiDAR和图像数据生成UGV的局部BEV感知图像。该方法缓解了地面视角图像与卫星地图之间的显著视角差异。然后将BEV图像和卫星地图投影到道路相似性空间中，计算归一化互相关（NCC）以评估匹配分数。最后，采用粒子滤波器估计车辆姿态的概率分布。通过与GNSS地面真值比较，我们的定位系统在10公里的长距离测试中表现出稳定性且无发散，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，即使在夜间条件下也能保持准确且稳定的全局定位，进一步验证了其鲁棒性和适应性。
英文摘要：
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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