Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching
<|reference_start|>Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching: Underwater object-level mapping requires incorporating visual foundation models to handle the uncommon and often previously unseen object classes encountered in marine scenarios. In this work, a metric of semantic uncertainty for open-set object detections produced by visual foundation models is calculated and then incorporated into an object-level uncertainty tracking framework. Object-level uncertainties and geometric relationships between objects are used to enable robust object-level loop closure detection for unknown object classes. The above loop closure detection problem is formulated as a graph-matching problem. While graph matching, in general, is NP-Complete, a solver for an equivalent formulation of the proposed graph matching problem as a graph editing problem is tested on multiple challenging underwater scenes. Results for this solver as well as three other solvers demonstrate that the proposed methods are feasible for real-time use in marine environments for the robust, open-set, multi-object, semantic-uncertainty-aware loop closure detection. Further experimental results on the KITTI dataset demonstrate that the method generalizes to large-scale terrestrial scenes.<|reference_end|>
@article{singh2024open-set,
    title={Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching},
    author={Kurran Singh and John J. Leonard},
    journal={arXiv preprint arXiv:2409.11555},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11555},
    primaryClass={cs.RO cs.CV}
}