Papers › CORAL: Colored structural representation for bi-modal place recognition
CORAL: Colored structural representation for bi-modal place recognition
Yiyuan Pan, Xuecheng Xu, Weijie Li, Yunxiang Cui, Yue Wang, Rong Xiong
Place recognition is indispensable for a drift-free localization system. Due to the variations of the environment, place recognition using single-modality has limitations. In this paper, we propose a bi-modal place recognition method, which can extract a compound global descriptor from the two modalities, vision and LiDAR. Specifically, we first build the elevation image generated from 3D points as a structural representation. Then, we derive the correspondences between 3D points and image pixels that are further used in merging the pixel-wise visual features into the elevation map grids. In this way, we fuse the structural features and visual features in the consistent bird-eye view frame, yielding a semantic representation, namely CORAL. And the whole network is called CORAL-VLAD. Comparisons on the Oxford RobotCar show that CORAL-VLAD has superior performance against other state-of-the-art methods. We also demonstrate that our network can be generalized to other scenes and sensor configurations on cross-city datasets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Place Recognition | Oxford RobotCar (LiDAR 4096 points+RGB) | CORAL | recall@top1 | 88.93 | #2 of 3 | Archive leaderboard | report |
| Visual Place Recognition | Oxford RobotCar (LiDAR 4096 points+RGB) | CORAL | recall@top1% | 96.13 | #2 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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