We introduce a novel architecture, UniCal, for Camera-to-LiDAR (C2L) extrinsic calibration which leverages self-attention mechanisms through a Transformer-based backbone network to infer the 6-degree of freedom (DoF) relative transformation between the sensors. Unlike previous methods, UniCal performs an early fusion of the input camera and LiDAR data by aggregating camera image channels and LiDAR mappings into a multi-channel unified representation before extracting their features jointly with a single-branch architecture. This single-branch architecture makes UniCal lightweight, which is desirable in applications with restrained resources such as autonomous driving. Through experiments, we show that UniCal achieves state-of-the-art results compared to existing methods. We also show that through transfer learning, weights learned on the calibration task can be applied to a calibration validation task without re-training the backbone.

翻译：我们提出了一种新颖的架构UniCal，用于相机到激光雷达（C2L）的外部参数标定。该方法利用基于Transformer骨干网络的自注意力机制，推断传感器之间的6自由度（DoF）相对变换。与以往方法不同，UniCal通过将相机图像通道与激光雷达映射聚合为多通道统一表示，在单分支架构下联合提取特征，实现了输入相机与激光雷达数据的早期融合。这种单分支架构使UniCal轻量化，适用于自动驾驶等资源受限的应用场景。实验表明，UniCal相比现有方法取得了最先进的结果。我们还证明，通过迁移学习，在标定任务上训练得到的权重可直接应用于标定验证任务，无需对骨干网络进行重新训练。