Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914951691501568 |
|---|---|
| author | Singh, Kurran Leonard, John J. |
| author_facet | Singh, Kurran Leonard, John J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11555 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching Singh, Kurran Leonard, John J. Robotics Computer Vision and Pattern Recognition 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. |
| title | Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.11555 |