RAMBA: 通过束调整的4D雷达建图
RAMBA: 4D Radar Mapping by Bundle Adjustment
AI总结：
提出RAMBA框架，利用束调整联合优化雷达帧状态，结合协方差加权几何残差、IMU预积分因子和雷达自速度约束，实现全局一致的4D雷达建图。
摘要：
4D雷达在机器人建图中越来越有吸引力，因为它提供距离、方位角、仰角和多普勒测量，同时在恶劣可见度条件下保持鲁棒性。尽管最近的雷达和雷达-惯性里程计方法已经实现了有前景的在线状态估计性能，但4D雷达的离线全局地图优化仍未得到充分探索。本文提出了RAMBA，一种用于全局一致4D雷达建图的雷达束调整框架。给定来自雷达-惯性里程计前端的初始位姿和雷达帧，RAMBA使用协方差加权几何残差、IMU预积分因子和雷达自速度约束联合优化雷达帧状态。几何残差通过跨选定帧形成基于体素的对应关系，并用点协方差加权每个残差，将成对GICP扩展到多帧优化。为了提高对漂移和重访的鲁棒性，RAMBA在对应关系形成过程中强制时间一致性，同时明确支持闭环约束。在ColoRadar和SNAIL Radar数据集上的实验表明，与雷达-惯性里程计和位姿图优化基线相比，RAMBA提高了地图一致性并通常提升了轨迹精度。
英文摘要：
4D radar is increasingly attractive for robotic mapping because it provides range, azimuth, elevation, and Doppler measurements while remaining robust in adverse visibility conditions. Although recent radar and radar--inertial odometry methods have achieved promising online state estimation performance, offline global map refinement for 4D radar remains underexplored. This paper presents RAMBA, a radar bundle-adjustment framework for globally consistent 4D radar mapping. Given initial poses and radar frames from a radar--inertial odometry front-end, RAMBA jointly refines radar frame states using covariance-weighted geometric residuals, IMU preintegration factors, and radar ego-velocity constraints. The geometric residuals extend pairwise GICP to a multi-frame optimization by forming voxel-based correspondences across selected frames and weighting each residual with point covariances. To improve robustness against drift and revisits, RAMBA enforces temporal consistency during correspondence formation while explicitly supporting loop-closure constraints. Experiments on the ColoRadar and SNAIL Radar datasets show that RAMBA improves map consistency and usually enhances trajectory accuracy over radar--inertial odometry and pose-graph optimization baselines.
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。
