@Article{cmc.2026.079663,
AUTHOR = {Xiangqin Chen},
TITLE = {CF2-SLAM: Conformal-Calibrated Foundation-Factor Graph SLAM across Modalities and Domains},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/26780},
ISSN = {1546-2226},
ABSTRACT = {Simultaneous localization and mapping (SLAM) must remain reliable when sensing suites and operating conditions vary across platforms and deployments. Beyond correspondence degradation, a dominant deployment failure mode is <i>misweighted</i> constraints: under distribution shift, uncertainty estimates can become miscalibrated, allowing a small set of overconfident factors to dominate iterative optimization and destabilize inference. This article presents conformal-calibrated foundation-factor graph SLAM (<mml:math id="mml-ieqn-1"><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant="normal">C</mml:mi><mml:mi mathvariant="normal">F</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mtext>-</mml:mtext><mml:mi>S</mml:mi><mml:mi>L</mml:mi><mml:mi>A</mml:mi><mml:mi>M</mml:mi></mml:mrow></mml:math>), a sensor-agnostic framework that combines frozen foundation representations with lightweight probabilistic factor heads that emit explicit residuals and covariances, and a classical factor-graph back-end for principled multi-modal fusion. To mitigate systematic misweighting under shift, an online conformal calibration layer is introduced to rescale factor covariances by aligning empirical residual quantiles with target quantiles on a per-factor-family basis. Loop closure is further integrated through foundation-descriptor retrieval for candidate proposal and conservative geometric verification for graph insertion, controlling false loop constraints without relying on dataset-specific place-recognition supervision. Across heterogeneous benchmarks spanning monocular, stereo, red-green-blue-depth (RGB-D), and visual-inertial settings, <mml:math id="mml-ieqn-2"><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant="normal">C</mml:mi><mml:mi mathvariant="normal">F</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mtext>-</mml:mtext><mml:mi>S</mml:mi><mml:mi>L</mml:mi><mml:mi>A</mml:mi><mml:mi>M</mml:mi></mml:mrow></mml:math> operates without retraining and shows improved robustness trends under zero-shot transfer, consistent with stabilized factor weighting.},
DOI = {10.32604/cmc.2026.079663}
}