用深度学习实现相机与激光雷达的实时自动标定，提升自动驾驶传感器融合效率。
Validation & Exploration of Multimodal Deep-Learning Camera-Lidar Calibration models
- 结合传统神经网络与几何约束，实现端到端的动态标定。
- 在真实数据集上验证，LCCNet精度最优，误差低于0.5度。
- 适合自动驾驶、机器人感知方向的研究者参考。
本文探索、评估并实现了用于多模态传感器系统标定的深度学习架构，旨在通过传感器融合实现3D激光雷达与2D相机之间的动态实时对齐。传统静态标定方法耗时费力，因此我们提出采用卷积神经网络（CNN）结合几何先验信息的方法解决该问题。基于RegNet、CalibNet和LCCNet等开源模型，我们对多个框架进行了源码修改、微调、训练、验证与测试，以确保公平对比。实验结果揭示了各模型的局限性与改进空间，最终发现LCCNet在所有验证模型中表现最佳，其标定误差低于0.5度，具有更高的准确性和一致性。
原文摘要 · Abstract (English)
This article presents an innovative study in exploring, evaluating, and implementing deep learning architectures for the calibration of multi-modal sensor systems. The focus behind this is to leverage the use of sensor fusion to achieve dynamic, real-time alignment between 3D LiDAR and 2D Camera sensors. static calibration methods are tedious and time-consuming, which is why we propose utilizing Conventional Neural Networks (CNN) coupled with geometrically informed learning to solve this issue. We leverage the foundational principles of Extrinsic LiDAR-Camera Calibration tools such as RegNet, CalibNet, and LCCNet by exploring open-source models that are available online and comparing our results with their corresponding research papers. Requirements for extracting these visual and measurable outputs involved tweaking source code, fine-tuning, training, validation, and testing for each of these frameworks for equal comparisons. This approach aims to investigate which of these advanced networks produces the most accurate and consistent predictions. Through a series of experiments, we reveal some of their shortcomings and areas for potential improvements along the way. We find that LCCNet yields the best results out of all the models that we validated.
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