Road Similarity-Based BEV-Satellite Image Matching for UGV Localization

Engineering notes

These results demonstrate superior performance compared to existing state-of-theart methods in the field.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

To address the challenge of autonomous unmanned ground vehicle (UGV) localization in Global navigation satellite system (GNSS) denied off-road environments, this study proposes a matching-based localization method that leverages bird’s-eye view (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 $\mathbf{1 0 ~ k m}$, achieving an average lateral error of only 0.89 meters and an average planar Euclidean error of 3.41 meters. These results demonstrate superior performance compared to existing state-of-theart methods in the field. Furthermore, it maintained accurate and stable global localization even under nighttime conditions, further validating its robustness and adaptability.

Links and sources

• Official / arXiv page
• PDF from original source

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