Multi-modal Loop Closure Detection with Foundation Models in Severely Unstructured Environments
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908636162293760 |
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| author | Gonzalez, Laura Alejandra Encinar Folkesson, John Triebel, Rudolph Giubilato, Riccardo |
| author_facet | Gonzalez, Laura Alejandra Encinar Folkesson, John Triebel, Rudolph Giubilato, Riccardo |
| contents | Robust loop closure detection is a critical component of Simultaneous Localization and Mapping (SLAM) algorithms in GNSS-denied environments, such as in the context of planetary exploration. In these settings, visual place recognition often fails due to aliasing and weak textures, while LiDAR-based methods suffer from sparsity and ambiguity. This paper presents MPRF, a multimodal pipeline that leverages transformer-based foundation models for both vision and LiDAR modalities to achieve robust loop closure in severely unstructured environments. Unlike prior work limited to retrieval, MPRF integrates a two-stage visual retrieval strategy with explicit 6-DoF pose estimation, combining DINOv2 features with SALAD aggregation for efficient candidate screening and SONATA-based LiDAR descriptors for geometric verification. Experiments on the S3LI dataset and S3LI Vulcano dataset show that MPRF outperforms state-of-the-art retrieval methods in precision while enhancing pose estimation robustness in low-texture regions. By providing interpretable correspondences suitable for SLAM back-ends, MPRF achieves a favorable trade-off between accuracy, efficiency, and reliability, demonstrating the potential of foundation models to unify place recognition and pose estimation. Code and models will be released at github.com/DLR-RM/MPRF. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_05404 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-modal Loop Closure Detection with Foundation Models in Severely Unstructured Environments Gonzalez, Laura Alejandra Encinar Folkesson, John Triebel, Rudolph Giubilato, Riccardo Computer Vision and Pattern Recognition Artificial Intelligence I.2.9; I.2.10 Robust loop closure detection is a critical component of Simultaneous Localization and Mapping (SLAM) algorithms in GNSS-denied environments, such as in the context of planetary exploration. In these settings, visual place recognition often fails due to aliasing and weak textures, while LiDAR-based methods suffer from sparsity and ambiguity. This paper presents MPRF, a multimodal pipeline that leverages transformer-based foundation models for both vision and LiDAR modalities to achieve robust loop closure in severely unstructured environments. Unlike prior work limited to retrieval, MPRF integrates a two-stage visual retrieval strategy with explicit 6-DoF pose estimation, combining DINOv2 features with SALAD aggregation for efficient candidate screening and SONATA-based LiDAR descriptors for geometric verification. Experiments on the S3LI dataset and S3LI Vulcano dataset show that MPRF outperforms state-of-the-art retrieval methods in precision while enhancing pose estimation robustness in low-texture regions. By providing interpretable correspondences suitable for SLAM back-ends, MPRF achieves a favorable trade-off between accuracy, efficiency, and reliability, demonstrating the potential of foundation models to unify place recognition and pose estimation. Code and models will be released at github.com/DLR-RM/MPRF. |
| title | Multi-modal Loop Closure Detection with Foundation Models in Severely Unstructured Environments |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.2.9; I.2.10 |
| url | https://arxiv.org/abs/2511.05404 |