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TRaIL-Odom: Tightly Coupled Continuous Time Radar-IMU-LiDAR Odometry with Adaptive Doppler Weighting — arXiv:2609.03561

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.03561
License (as archived): CC0-1.0
Archived: 2026-10-17T02:57:28.224Z
SHA-256 of the archived text: f3365e1c768c66b0c9fc211156b7ae99a6131d8a4b8d319a3a2560389bca3e65
Stable URL of this page: https://metraipolis.com/documento/dc_fa3e5f9641cd

How to cite

TRaIL-Odom: Tightly Coupled Continuous Time Radar-IMU-LiDAR Odometry with Adaptive Doppler Weighting — arXiv:2609.03561 — arXiv (en), https://arxiv.org/abs/2609.03561 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fa3e5f9641cd (Metraipolis archive dc_fa3e5f9641cd, sha256 f3365e1c768c66b0c9fc211156b7ae99a6131d8a4b8d319a3a2560389bca3e65)

BibTeX

@misc{dcfa3e5f9641cd,
  title = {TRaIL-Odom: Tightly Coupled Continuous Time Radar-IMU-LiDAR Odometry with Adaptive Doppler Weighting — arXiv:2609.03561},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.03561},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fa3e5f9641cd; sha256 f3365e1c768c66b0c9fc211156b7ae99a6131d8a4b8d319a3a2560389bca3e65},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.03561. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-04 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.03561 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T02:33:25.384Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-04?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).