用捆绑调整优化4D雷达地图，提升精度与一致性
RAMBA: 4D Radar Mapping by Bundle Adjustment
- 通过多帧体素匹配和协方差加权残差优化雷达位姿
- 在ColoRadar和SNAIL数据集上显著改善地图一致性
- 适合需要高鲁棒性雷达建图的机器人系统
4D雷达因其在恶劣视觉条件下仍能提供距离、方位角、俯仰角和多普勒信息，日益成为机器人建图的理想选择。尽管近期雷达-惯性里程计方法已实现良好的在线状态估计性能，但针对4D雷达的离线全局地图优化仍研究不足。本文提出RAMBA，一种基于捆绑调整的4D雷达全局建图框架。给定雷达-惯性里程计前端输出的初始位姿和雷达帧，RAMBA联合优化雷达帧状态，利用协方差加权几何残差、IMU预积分因子及雷达自运动约束。几何残差通过在选定帧间构建体素对应关系，扩展了成对GICP至多帧优化，并以点协方差加权。为增强对漂移和重访的鲁棒性，RAMBA在对应关系生成中引入时序一致性约束，并显式支持回环闭合。在ColoRadar和SNAIL Radar数据集上的实验表明，RAMBA显著提升了地图一致性，通常优于雷达-惯性里程计与位姿图优化基线。
原文摘要 · Abstract (English)
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.
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