Metraipolis › archive › dc_bdcd03abea5e

Smart Fusion for Adaptive Visual-IMU Weighting in cuVSLAM Sparse Bundle Adjustment — arXiv:2610.20226

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.20226
License (as archived): CC0-1.0
Archived: 2026-10-15T21:58:02.502Z
SHA-256 of the archived text: 950544d8c3fe7497d0f3ab9b0c1f2d5ededd8136d531c4f6c3a1880c9dccea54
Stable URL of this page: https://metraipolis.com/documento/dc_bdcd03abea5e

How to cite

Smart Fusion for Adaptive Visual-IMU Weighting in cuVSLAM Sparse Bundle Adjustment — arXiv:2610.20226 — arXiv (en), https://arxiv.org/abs/2610.20226 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bdcd03abea5e (Metraipolis archive dc_bdcd03abea5e, sha256 950544d8c3fe7497d0f3ab9b0c1f2d5ededd8136d531c4f6c3a1880c9dccea54)

BibTeX

@misc{dcbdcd03abea5e,
  title = {Smart Fusion for Adaptive Visual-IMU Weighting in cuVSLAM Sparse Bundle Adjustment — arXiv:2610.20226},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.20226},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bdcd03abea5e; sha256 950544d8c3fe7497d0f3ab9b0c1f2d5ededd8136d531c4f6c3a1880c9dccea54},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.20226. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In cuVSLAM, visual and inertial measurement unit (IMU) residuals are fused in sparse bundle adjustment (SBA) with a weighting fixed by pre-calibrated noise covariances, leaving no mechanism to adapt the visual-inertial balance at runtime. We introduce a scalar $\alpha\geq1$ that upscales the visual information matrix relative to the IMU preintegration constraint. Across the candidate set $\{1.0,2.0,5.0,10.0,20.0\}$, the resulting trajectories form two empirical performance regimes. The IMU-TRUST regime contains $\alpha\in\{1,2\}$ near the calibrated baseline, while the VISION-TRUST regime contains $\alpha\in\{10,20\}$. The intermediate value $\alpha=5$ is treated as a transition case. Differences within each regime are small relative to those observed between the regimes. This structure motivates reducing $\alpha$-selection to a binary decision. We train a lightweight Visual Quality Estimator (VQE), implemented as a multilayer perceptron with 914 trainable parameters. It maps a 10-dimensional visual quality feature vector assembled from statistics already maintained by the tracker to a per-SBA regime prediction. The proposed method reduces mean RMSE APE by $27.7\%$ and by $14.9\%$ under leave-one-out cross-validation (LOOCV) on EuRoC MAV and TUM-VI Room, respectively.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.20226 — 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 2026-10-15T21:38:44.498Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).