Abstract: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.
| Comments: | 5 pages, 2 figures, to present in ISPRS2026 Thematic Session 10 on Radar Perception |
| Subjects: | Robotics (cs.RO) |
| Cite as: | arXiv:2605.25041 [cs.RO] |
| (or arXiv:2605.25041v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2605.25041 arXiv-issued DOI via DataCite (pending registration) |
From: Jianzhu Huai [view email]
[v1]
Sun, 24 May 2026 12:38:18 UTC (11,902 KB)
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