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arXiv · 2610.27310

RadGeoLoc: Radar Geolocalization via Particle Filtering

Shaunak Kolhe·Peng Jiang·Kasi Viswanath·Srikanth Saripalli
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Abstract

In real-time autonomous field applications, GNSS is often unreliable and inaccurate. A possible solution is localizing against a prebuilt map like a satellite image, as it is georeferenced and already surveyed. In this work, we localize a spinning radar measurement against overhead RGB imagery with a learned observation model. We use a radar representation pretrained by masked reconstruction and align it to features from a satellite image. A particle filter propagated by radar odometry proposes candidate poses, and our observation model provides a likelihood over these poses. We evaluate our method over 31 distinct routes, over 8 datasets spanning both urban and offroad conditions using only ground radar and a coarse local prior. We also examine the robustness over changing weather using repeated traversals over the same route over 13 months, in both urban and offroad conditions.

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Shaunak Kolhe, Peng Jiang, Kasi Viswanath, Srikanth Saripalli. 2026-10-19. RadGeoLoc: Radar Geolocalization via Particle Filtering. https://arxiv.org/abs/2610.27310

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