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.

翻译：本文提出BEV-Patch-PF，一种无需GPS的序列化地理定位系统，该系统将粒子滤波器与学习的鸟瞰图特征图和航拍特征图相结合。我们利用车载RGB图像与深度图像构建BEV特征图。针对每个三维自由度粒子位姿假设，我们从近似位置附近查询的局部航拍图像所计算的航拍特征图中裁剪出对应区域。BEV-Patch-PF通过匹配BEV特征与航拍区域特征来计算每个粒子的对数似然。在两个真实世界越野数据集上的实验表明：在已观测路线上，本方法的绝对轨迹误差较基于检索的基线方法降低9.7倍；在未观测路线上降低6.6倍，同时在茂密树冠与阴影环境下仍保持定位精度。该系统在NVIDIA Tesla T4平台上能以10赫兹频率实时运行，具备实际机器人部署能力。