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Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam.IEEE transactions on robotics, 37(6):1874–1890, 2021

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it

fields

cs.RO 2 cs.CV 1

years

2026 3

verdicts

UNVERDICTED 3

representative citing papers

Change-Robust Online Spatial-Semantic Topological Mapping

cs.RO · 2026-05-04 · unverdicted · novelty 6.0

CROSS replaces globally consistent metric maps with a pose-aware topological graph of RGB-D keyframes and maintains a bounded Gaussian-mixture belief over poses via sequential hypothesis testing in SE(3) to achieve change-robust spatial-semantic mapping and navigation.

Deploy DINO with Many-to-Many Association

cs.CV · 2026-04-26 · unverdicted · novelty 5.0

DINO features combined with many-to-many association and the proposed Harmonic Consensus Maximization enable general visual features to compete with specialized models on out-of-distribution image matching and camera pose estimation.

citing papers explorer

Showing 3 of 3 citing papers.

  • Change-Robust Online Spatial-Semantic Topological Mapping cs.RO · 2026-05-04 · unverdicted · none · ref 6

    CROSS replaces globally consistent metric maps with a pose-aware topological graph of RGB-D keyframes and maintains a bounded Gaussian-mixture belief over poses via sequential hypothesis testing in SE(3) to achieve change-robust spatial-semantic mapping and navigation.

  • UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception cs.RO · 2026-04-15 · unverdicted · none · ref 3

    UMI-3D integrates LiDAR into the UMI hardware for robust multimodal 3D perception in manipulation demonstrations, yielding higher policy success rates and enabling previously infeasible tasks like deformable object handling.

  • Deploy DINO with Many-to-Many Association cs.CV · 2026-04-26 · unverdicted · none · ref 7

    DINO features combined with many-to-many association and the proposed Harmonic Consensus Maximization enable general visual features to compete with specialized models on out-of-distribution image matching and camera pose estimation.