用深度学习实现摄像头、激光雷达和4D雷达的无目标联合标定
CLRNet: Targetless Extrinsic Calibration for Camera, Lidar and 4D Radar Using Deep Learning
- 基于多模态深度网络，端到端联合标定三种传感器
- 在两个数据集上将平移和旋转误差降低至少50%
- 适用于自动驾驶多传感器系统部署，尤其适合雷达稀疏数据场景
本文针对摄像头、激光雷达与4D雷达的外参标定问题提出CLRNet，一种新型的端到端深度学习标定网络。由于雷达数据稀疏，其外参标定仍具挑战性。该方法通过等距投影、基于相机的深度图预测、额外雷达通道，并利用共享特征空间与环路闭合损失，实现三者联合标定或任意两两组合标定。在View-of-Delft和Dual-Radar数据集上的大量实验表明，相比现有最先进方法，该模型在中位平移与旋转误差上均至少降低50%。最后，我们评估了模型在不同数据集间的域迁移能力。代码将在论文录用后公开于https://github.com/tudelft-iv。
原文摘要 · Abstract (English)
In this paper, we address extrinsic calibration for camera, lidar, and 4D radar sensors. Accurate extrinsic calibration of radar remains a challenge due to the sparsity of its data. We propose CLRNet, a novel, multi-modal end-to-end deep learning (DL) calibration network capable of addressing joint camera-lidar-radar calibration, or pairwise calibration between any two of these sensors. We incorporate equirectangular projection, camera-based depth image prediction, additional radar channels, and leverage lidar with a shared feature space and loop closure loss. In extensive experiments using the View-of-Delft and Dual-Radar datasets, we demonstrate superior calibration accuracy compared to existing state-of-the-art methods, reducing both median translational and rotational calibration errors by at least 50%. Finally, we examine the domain transfer capabilities of the proposed network and baselines, when evaluating across datasets. The code will be made publicly available upon acceptance at: https://github.com/tudelft-iv.
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