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This paper introduces TRaIL-Odom, a novel tightly coupled Radar-IMU-LiDAR odometry framework that incorporates two adaptive Doppler reweighting modules to address the limitations of fixed residual weights in existing radar-LiDAR fusion methods. By dynamically adjusting radar contributions based on LiDAR geometric anisotropy and identifying weak translational directions, the framework significantly enhances performance in geometrically degenerate scenes. The results demonstrate an impressive reduction in RMSE ATE and RTE by 86.0% and 78.5% respectively, compared to traditional fixed-weight approaches across 13 evaluated sequences.
Adaptive weighting in TRaIL-Odom leads to an 86% reduction in localization error in challenging geometrically degenerate environments.
Existing radar-LiDAR fusion methods rely on fixed residual weights, even though the informativeness of radar Doppler and LiDAR geometry is scan- and direction-dependent, leading to uniform radar weighting that misallocates Doppler information across translational directions. To address this limitation, we propose two degeneracy-aware Doppler reweighting modules within a tightly coupled Radar-IMU-LiDAR odometry framework: per-point radar reweighting and scan-wise radar gain scheduling. Since geometric degeneracy is directional, we first identify weak translational directions from the LiDAR geometry and reweight individual radar Doppler constraints based on their alignment with the weak subspace. We further adjust the overall radar contribution using LiDAR geometric anisotropy such that radar is emphasized when LiDAR observability is poor and suppressed when LiDAR constraints are already reliable. Across 13 evaluated sequences, TRaIL-Odom achieves state-of-the-art overall performance, with clear advantages in geometrically degenerate scenes. In ablation experiments on three degenerate sequences, combining the two adaptive weighting modules reduces RMSE ATE and RTE by 86.0% and 78.5% relative to the fixed-weight baseline. We make our code and an accompanying dataset publicly available at https://github.com/ChiyunNoh/TRaIL-Odom.
