Robotics: Science and Systems XXII

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

Daniil Lisus, Cedric Le Gentil, Timothy Barfoot

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.

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Bibtex:

  
@INPROCEEDINGS{LisusD-RSS-26, 
    AUTHOR    = {Daniil Lisus AND Cedric Le Gentil AND Timothy Barfoot}, 
    TITLE     = {{Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.046} 
}