EEPNet: Efficient Edge Pixel-based Matching Network for Cross-Modal Dynamic Registration between LiDAR and Camera

• URL: http://arxiv.org/abs/2409.19305v1
• Date: Sat, 28 Sep 2024 10:28:28 GMT
• Title: EEPNet: Efficient Edge Pixel-based Matching Network for Cross-Modal Dynamic Registration between LiDAR and Camera
• Authors: Yuanchao Yue, Hui Yuan, Suai Li, Qi Jiang,
• Abstract summary: Multisensor fusion is essential for autonomous vehicles to accurately perceive, analyze, and plan their trajectories within complex environments. Current methods for registering LiDAR point clouds with images face significant challenges due to inherent differences and computational overhead. We propose EEPNet, an advanced network that leverages modality maps obtained from point cloud projections to enhance registration accuracy.
• Score: 6.817117737186402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multisensor fusion is essential for autonomous vehicles to accurately perceive, analyze, and plan their trajectories within complex environments. This typically involves the integration of data from LiDAR sensors and cameras, which necessitates high-precision and real-time registration. Current methods for registering LiDAR point clouds with images face significant challenges due to inherent modality differences and computational overhead. To address these issues, we propose EEPNet, an advanced network that leverages reflectance maps obtained from point cloud projections to enhance registration accuracy. The introduction of point cloud projections substantially mitigates cross-modality differences at the network input level, while the inclusion of reflectance data improves performance in scenarios with limited spatial information of point cloud within the camera's field of view. Furthermore, by employing edge pixels for feature matching and incorporating an efficient matching optimization layer, EEPNet markedly accelerates real-time registration tasks. Experimental validation demonstrates that EEPNet achieves superior accuracy and efficiency compared to state-of-the-art methods. Our contributions offer significant advancements in autonomous perception systems, paving the way for robust and efficient sensor fusion in real-world applications.

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