[2605.07041] Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization
[Submitted on 7 May 2026]
Title:Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization
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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 this https URL.
Comments: Accepted for presentation at RSS 2026
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.07041 [cs.RO]
(or arXiv:2605.07041v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.07041
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Daniil Lisus [view email] [v1] Thu, 7 May 2026 23:41:16 UTC (17,765 KB)
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