UniLoop: Unified Loop Closure Detection from Retrieval to Geometric Verification and Relative Pose Estimation

Abstract:Loop closure detection conventionally cascades separate models for global retrieval, geometric verification, and relative pose estimation, leading to redundant computation and disconnected visual representations. We propose UniLoop, a unified framework that reuses the hierarchical representations of the pretrained geometric foundation model throughout this pipeline. A shared image encoder produces features for compact global descriptors; the same features are passed to the cross-view decoder for pairwise verification; and the original geometric predictions provide relative camera constraints for pose-graph optimization. Experiments on place recognition, loop verification, and relative pose estimation show that UniLoop improves loop-closure robustness and accuracy while reducing redundant backbone computation relative to modular pipelines. UniLoop achieves the best Recall@10 on MSLS (96.6) and Pitts30k (99.3) among the compared methods, while its verification branch reaches 95.63 AP and 96.29 ROC-AUC, surpassing the existing geometric verification baselines.

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2611.06378 [cs.RO]

(or arXiv:2611.06378v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2611.06378 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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