Place recognition is indispensable for  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  compound global descriptor from the two modalities, vision and LiDAR. Specifically, we build elevation image generated from point cloud modality as a discriminative structural representation. Based on the 3D information , we derive the correspondences between 3D points and image pixels , by which the pixel-wise visual features can be inserted 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 feature representationwith sensible geometry , namely CORAL.  Comparisons on the Oxford RobotCar show that CORAL 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 using cross-city datasets.
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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single modality
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build
first build the
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point cloud modality as a discriminative
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Based on the 3D information
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feature representationwith sensible geometry
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And the whole network is called CORAL-VLAD.
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