用鸟瞰图特征匹配实现无GPS越野定位，精度远超传统方法。
BEV-Patch-PF: Particle Filtering with BEV-Aerial Feature Matching for Off-Road Geo-Localization
- 结合粒子滤波与鸟瞰特征匹配，实现无GPS定位
- 在已见和未见路径上分别降低9.7倍和6.6倍轨迹误差
- 适合复杂遮挡环境下的机器人实时部署
我们提出BEV-Patch-PF，一种不依赖GPS的序列化越野地理定位系统，该系统将粒子滤波与学习得到的鸟瞰视图（BEV）及航拍特征地图相结合。通过车载RGB和深度图像构建BEV特征图。对于每个3-自由度粒子姿态假设，从围绕近似位置查询的局部航拍图像计算出的航拍特征图中裁剪对应区域。通过匹配BEV特征与航拍块特征，计算每个粒子的对数似然值。在两个真实世界越野数据集上，本方法在已见路径上的绝对轨迹误差（ATE）比基于检索的基线低9.7倍，在未见路径上低6.6倍，且在密集树冠和阴影环境下仍保持高精度。系统在NVIDIA Tesla T4上实现实时运行，帧率达10 Hz，支持实际机器人部署。
原文摘要 · Abstract (English)
We propose BEV-Patch-PF, a GPS-free sequential geo-localization system that integrates a particle filter with learned bird's-eye-view (BEV) and aerial feature maps. From onboard RGB and depth images, we construct a BEV feature map. For each 3-DoF particle pose hypothesis, we crop the corresponding patch from an aerial feature map computed from a local aerial image queried around the approximate location. BEV-Patch-PF computes a per-particle log-likelihood by matching the BEV feature to the aerial patch feature. On two real-world off-road datasets, our method achieves 9.7x lower absolute trajectory error (ATE) on seen routes and 6.6x lower ATE on unseen routes than a retrieval-based baseline, while maintaining accuracy under dense canopy and shadow. The system runs in real time at 10 Hz on an NVIDIA Tesla T4, enabling practical robot deployment.
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