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 ATOP 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 Point-PSO and Pose-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.
ATOP: An Attention-to-Optimization Approach for Automatic LiDAR-Camera Calibration via Cross-Modal Object Matching
IEEE Transactions on Intelligent Vehicles ; 8 , 1 ; 696-708
2023-01-01
7711872 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
British Library Conference Proceedings | 2004
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