RAMBA: 4D Radar Mapping by Bundle Adjustment

• URL: http://arxiv.org/abs/2605.25041v1
• Date: Sun, 24 May 2026 12:38:18 GMT
• Title: RAMBA: 4D Radar Mapping by Bundle Adjustment
• Abstract summary: This paper presents RAMBA, a radar bundle-adjustment framework for globally consistent 4D radar mapping.<n>Experiments on the ColoRadar and SNAIL Radar datasets show that RAMBA improves map consistency and usually enhances trajectory accuracy.
• Score: 3.8603693015738205
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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