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논문 기본 정보

저자정보
(Kookmin University) (Kookmin University)
저널정보
한국통신학회 한국통신학회논문지 한국통신학회논문지 제50권 제8호
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    초록·키워드

    The integration of cameras and light detection and ranging (LiDAR) in multisensor systems has made precise extrinsic calibration increasingly critical. Although deep learning methods have demonstrated potential, they typically struggle to fuse RGB images with LiDAR depth data effectively in dynamic or complex environments, particularly for models with high parameter counts and computational overhead. We propose a framework that overcomes these challenges by dynamically leveraging multimodal data with relevant and fewer parameters to deliver real time, robust, and scalable calibration. The proposed framework fully exploits the complementary strengths of both sensors while ensuring low resource consumption and fast inference, making it ideally suited for deployment in resource-constrained autonomous systems.

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      UCI(KEPA) : I410-151-25-02-093867816