RAMBA: 4D Radar Mapping by Bundle Adjustment
For robotic mapping in adverse visibility, this paper addresses the underexplored problem of offline global map refinement for 4D radar, offering a method that enhances consistency and accuracy.
RAMBA introduces a bundle adjustment framework for 4D radar mapping that jointly refines radar frame states using geometric residuals, IMU preintegration, and ego-velocity constraints, achieving improved map consistency and trajectory accuracy over baselines on ColoRadar and SNAIL Radar datasets.
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.