Dr-BA：直接雷达束调整与定位的可分离优化
Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization
AI总结：
本文提出Dr-BA框架，通过直接处理2D旋转雷达强度图像实现雷达束调整与定位，利用雷达抗雨优势，解决稠密地图与传感器姿态联合估计问题，实现最先进的雷达束调整与跨会话定位性能。
摘要：
本文提出Dr-BA框架，通过直接处理2D旋转雷达强度图像实现雷达束调整与定位，利用雷达抗雨优势，解决稠密地图与传感器姿态联合估计问题，实现最先进的雷达束调整与跨会话定位性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
