Robust Radar-IMU-LiDAR Odometry Across Diverse Environments
Fuses asynchronous radar Doppler, IMU, and LiDAR measurements in a continuous-time B-spline optimization framework for robust odometry across diverse real-world 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.
Fuses asynchronous radar Doppler, IMU, and LiDAR measurements in a continuous-time B-spline optimization framework for robust odometry across diverse real-world environments.
Identifies weak translational directions from LiDAR geometry and reallocates individual radar Doppler constraints toward the weak subspace while limiting influence along well-constrained directions.
Adjusts the overall Doppler contribution using LiDAR point-cloud anisotropy, emphasizing radar under severe degeneracy and suppressing unnecessary influence in well-constrained scans.
We release a real-world multi-sensor dataset designed to evaluate odometry under geometrically degenerate conditions.
Long, repetitive tunnel geometry with limited translational constraints.
Open park scenes with sparse structures and weak geometric observability.
Open-field trajectories with sparse and distant geometric features.
ATE [m] / RTE [m/m]. Lower is better; × indicates failure.
| Method | BikeTunnel1 | BikeTunnel2 | Park1 | Park2 | Airfield1 | Airfield2 |
|---|---|---|---|---|---|---|
| GenZ-ICP | × | × | × | × | × | × |
| FAST-LIO2 | × | 66.072 / 1.829 | 2.296 / 0.234 | 11.354 / 0.299 | 0.681 / 0.216 | 1.894 / 0.194 |
| SuperOdom | × | × | × | × | × | × |
| DLIO | × | × | 1.008 / 0.162 | 1.219 / 0.159 | 0.627 / 0.141 | 0.942 / 0.139 |
| RESPLE | × | × | × | × | 0.611 / 0.115 | 0.464 / 0.110 |
| DR-LRIO | 1.881 / 0.069 | 0.605 / 0.097 | 0.853 / 0.073 | 1.820 / 0.061 | 1.399 / 0.062 | 2.485 / 0.067 |
| GaRLIO | 2.507 / 0.097 | 1.558 / 0.250 | 0.423 / 0.035 | 4.602 / 0.039 | 0.773 / 0.028 | 0.580 / 0.025 |
| Ours-LIO | × | 12.352 / 0.546 | × | × | 9.627 / 0.180 | 0.127 / 0.027 |
| Ours-RLIO | 1.532 / 0.050 | 0.485 / 0.047 | 1.010 / 0.033 | 1.524 / 0.047 | 0.183 / 0.026 | 0.142 / 0.024 |
| Method | Downstair | CorriLoop | BiCorridor | SlopeStair | Tunnel | Overpass | Quad |
|---|---|---|---|---|---|---|---|
| GenZ-ICP | 1.208 / 0.055 | 0.728 / 0.063 | 0.569 / 0.057 | 1.966 / 0.050 | 0.891 / 0.040 | 1.346 / 0.062 | 4.239 / 0.042 |
| FAST-LIO2 | 0.459 / 0.036 | 0.336 / 0.049 | 0.767 / 0.040 | 0.867 / 0.030 | 0.525 / 0.031 | 1.067 / 0.036 | 3.616 / 0.040 |
| SuperOdom | 1.011 / 0.057 | 2.927 / 0.219 | 4.372 / 0.376 | 1.531 / 0.043 | 3.579 / 0.132 | 1.286 / 0.061 | 3.665 / 0.044 |
| DLIO | 0.887 / 0.059 | 0.284 / 0.064 | × | 0.694 / 0.065 | 0.791 / 0.055 | 0.954 / 0.067 | 3.464 / 0.062 |
| RESPLE | 0.471 / 0.044 | 0.152 / 0.050 | 0.508 / 0.073 | 1.183 / 0.042 | 1.214 / 0.030 | 0.912 / 0.040 | × |
| DR-LRIO | 0.565 / 0.045 | 0.444 / 0.053 | 1.051 / 0.039 | 2.242 / 0.044 | 1.700 / 0.037 | 0.621 / 0.046 | 2.786 / 0.044 |
| GaRLIO | 0.225 / 0.147 | 0.223 / 0.055 | × | 5.040 / 0.745 | 0.506 / 0.117 | × | × |
| Ours-LIO | 0.202 / 0.037 | 0.128 / 0.045 | 0.322 / 0.041 | 1.423 / 0.031 | 1.085 / 0.028 | 0.836 / 0.032 | 2.946 / 0.036 |
| Ours-RLIO | 0.213 / 0.035 | 0.139 / 0.044 | 0.279 / 0.036 | 1.374 / 0.029 | 1.017 / 0.028 | 0.813 / 0.034 | 2.374 / 0.036 |
@article{noh2026trailodom,
title={TRaIL-Odom: Tightly Coupled Continuous Time Radar-IMU-LiDAR Odometry with Adaptive Doppler Weighting},
author={Noh, Chiyun and Tuna, Turcan and Talbot, William and Hutter, Marco and Kneip, Laurent and Kim, Ayoung},
journal={IEEE Robotics and Automation Letters},
year={2026},
url={https://chiyunnoh.github.io/TRaIL-Odom/}
}