BEV-Patch-PF: Particle Filtering with BEV-Aerial Feature Matching for Off-Road Geo-Localization

Abstract

Localizing ground robots against aerial imagery provides a critical capability for autonomous navigation, especially in environments where GPS is unreliable or unavailable. This task is challenging due to large viewpoint differences and substantial environmental variability. Most prior methods localize each frame independently, using either global-descriptor retrieval or spatial feature alignment, which leaves them vulnerable to ambiguity and multi-modal pose hypotheses. While sequential reasoning can mitigate this uncertainty, adapting existing per-frame pipelines for sequential use introduces unfavorable trade-offs among accuracy, memory, and computation that limit their practical deployment. We propose BEV-Patch-PF, a vision-only, GPS-free sequential geo-localization system that integrates particle filtering with learned bird’s-eye-view (BEV) and aerial feature maps. For each 3-DoF particle pose hypothesis, we crop the corresponding patch from an aerial feature map computed from a local aerial image centered on the predicted pose. The resulting BEV–aerial feature match defines a per-particle log-likelihood for particle-filter updates. In addition, we learn a frame-level uncertainty estimate that adaptively flattens the observation likelihood for unreliable observations, preventing overconfident particle collapse in ambiguous regions. On two real-world off-road datasets, our method achieves 9.7 lower absolute trajectory error (ATE) on seen routes and 6.6 lower ATE on unseen routes than a retrieval-based baseline, while remaining robust under partial canopy cover and shadowing. The system runs in real time at 10 Hz on an NVIDIA Tesla T4, enabling practical robot deployment.