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• W4285121103 abstract "Due to the difference of data modalities, it’s a very challenging task to find the feature correspondences between 2D and 3D data in LiDAR-Camera calibration. In existing works, the establishment of the cross-model correspondence is always simplified by specifically designing artificial targets or restricting the region of searching correspondences with the help of initial extrinsic parameters. To achieve automatic LiDAR-Camera calibration without prior knowledge, we propose a novel self-adaptive LiDAR-Camera calibration approach named <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>ATOP</b> which realizes a cascaded procedure of ATtention-to-OPtimization. In the attention stage, an attention-based object-level matching network called Cross-Modal Matching Network (CMON) is designed for finding the overlapped FOV(Field of View) between camera and LiDAR, and producing 2D-3D object-level correspondences. In the optimization stage, two cascaded PSO-based (Particle Swarm Optimization) algorithms, namely <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>Point</i> -PSO and <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>Pose</i> -PSO, are designed to estimate the LiDAR-Camera extrinsic parameters. Different from previous works, the proposed calibration method does not require any artificial targets or initial pose guesses, therefore it can be applied to achieve online self-adaptive LiDAR-Camera calibration. Besides, this is the first work, to our best knowledge, to achieve object-level matching between uncalibrated camera and LiDAR data. Experimental results on both the collected datasets and KITTI datasets demonstrate the effectiveness of the proposed method." @default.
• W4285121103 created "2022-07-14" @default.
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• W4285121103 date "2023-01-01" @default.
• W4285121103 modified "2023-10-17" @default.
• W4285121103 title "ATOP: An Attention-to-Optimization Approach for Automatic LiDAR-Camera Calibration via Cross-Modal Object Matching" @default.
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• W4285121103 doi "https://doi.org/10.1109/tiv.2022.3184976" @default.
• W4285121103 hasPublicationYear "2023" @default.
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