Semantic-Enhanced Cross-Modal Place Recognition for Robust Robot Localization
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916952978489344 |
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| author | Lin, Yujia Evans, Nicholas |
| author_facet | Lin, Yujia Evans, Nicholas |
| contents | Ensuring accurate localization of robots in environments without GPS capability is a challenging task. Visual Place Recognition (VPR) techniques can potentially achieve this goal, but existing RGB-based methods are sensitive to changes in illumination, weather, and other seasonal changes. Existing cross-modal localization methods leverage the geometric properties of RGB images and 3D LiDAR maps to reduce the sensitivity issues highlighted above. Currently, state-of-the-art methods struggle in complex scenes, fine-grained or high-resolution matching, and situations where changes can occur in viewpoint. In this work, we introduce a framework we call Semantic-Enhanced Cross-Modal Place Recognition (SCM-PR) that combines high-level semantics utilizing RGB images for robust localization in LiDAR maps. Our proposed method introduces: a VMamba backbone for feature extraction of RGB images; a Semantic-Aware Feature Fusion (SAFF) module for using both place descriptors and segmentation masks; LiDAR descriptors that incorporate both semantics and geometry; and a cross-modal semantic attention mechanism in NetVLAD to improve matching. Incorporating the semantic information also was instrumental in designing a Multi-View Semantic-Geometric Matching and a Semantic Consistency Loss, both in a contrastive learning framework. Our experimental work on the KITTI and KITTI-360 datasets show that SCM-PR achieves state-of-the-art performance compared to other cross-modal place recognition methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13474 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic-Enhanced Cross-Modal Place Recognition for Robust Robot Localization Lin, Yujia Evans, Nicholas Computer Vision and Pattern Recognition Ensuring accurate localization of robots in environments without GPS capability is a challenging task. Visual Place Recognition (VPR) techniques can potentially achieve this goal, but existing RGB-based methods are sensitive to changes in illumination, weather, and other seasonal changes. Existing cross-modal localization methods leverage the geometric properties of RGB images and 3D LiDAR maps to reduce the sensitivity issues highlighted above. Currently, state-of-the-art methods struggle in complex scenes, fine-grained or high-resolution matching, and situations where changes can occur in viewpoint. In this work, we introduce a framework we call Semantic-Enhanced Cross-Modal Place Recognition (SCM-PR) that combines high-level semantics utilizing RGB images for robust localization in LiDAR maps. Our proposed method introduces: a VMamba backbone for feature extraction of RGB images; a Semantic-Aware Feature Fusion (SAFF) module for using both place descriptors and segmentation masks; LiDAR descriptors that incorporate both semantics and geometry; and a cross-modal semantic attention mechanism in NetVLAD to improve matching. Incorporating the semantic information also was instrumental in designing a Multi-View Semantic-Geometric Matching and a Semantic Consistency Loss, both in a contrastive learning framework. Our experimental work on the KITTI and KITTI-360 datasets show that SCM-PR achieves state-of-the-art performance compared to other cross-modal place recognition methods. |
| title | Semantic-Enhanced Cross-Modal Place Recognition for Robust Robot Localization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.13474 |