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

TL;DR

Dr-BA introduces a novel radar bundle adjustment framework that directly uses full radar intensity images for dense mapping and localization, offering robustness in all weather conditions.

Contribution

It presents the first separable optimization approach for radar BA that jointly estimates dense maps and poses, extending to direct radar localization.

Findings

Achieves state-of-the-art radar BA performance

Demonstrates robust localization over 200 km of diverse routes

Provides publicly available implementation for the community

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.