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arXiv · 2605.07041

Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization

Daniil Lisus·Cedric Le Gentil·Timothy D. Barfoot
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Abstract

This paper introduces Dr-BA, a first-of-its-kind radar bundle adjustment (BA) framework that operates directly on 2D spinning radar intensity images. Unlike camera or lidar sensors, radar is largely unaffected by precipitation, making it a critical modality for autonomous systems that require all-weather robustness. Existing state estimation approaches using spinning radar typically extract sparse point clouds from range-azimuth-intensity measurements and apply point cloud alignment techniques to estimate vehicle motion, scene structure, or to localize within an existing map. In contrast, Dr-BA uses the full radar returns from multiple scans to jointly estimate dense maps and sensor poses. By formulating the problem as a separable optimization, we derive an efficient and general solution that decouples pose estimation from mapping. In addition to solving the BA problem, this formulation naturally extends to direct radar-only localization (DRL) within a previously built map. Dr-BA achieves state-of-the-art radar-based BA and cross-session localization performance, demonstrated on more than 200 km of on-road data across five distinct routes. Our implementation is publicly available at https://github.com/utiasASRL/dr_ba.

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Daniil Lisus, Cedric Le Gentil, Timothy D. Barfoot. 2026-05-07. Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization. https://arxiv.org/abs/2605.07041

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