Extrinsic Calibration represents the cornerstone of autonomous driving. Its accuracy plays a crucial role in the perception pipeline, as any errors can have implications for the safety of the vehicle. Modern sensor systems collect different types of data from the environment, making it harder to align the data. To this end, we propose a target-based extrinsic calibration system tailored for a multi-LiDAR and multi-camera sensor suite. This system enables cross-calibration between LiDARs and cameras with limited prior knowledge using a custom ChArUco board and a tailored nonlinear optimization method. We test the system with real-world data gathered in a warehouse. Results demonstrated the effectiveness of the proposed method, highlighting the feasibility of a unique pipeline tailored for various types of sensors.
翻译：外参标定是自动驾驶技术的基石，其精度对感知流程至关重要，任何误差都可能影响车辆的安全性。现代传感器系统从环境中采集不同类型的数据，使得数据对齐更为困难。为此，我们提出一种专为多激光雷达和多相机传感器套件设计的基于标定板的外参标定系统。该系统通过定制的ChArUco标定板和专门的非线性优化方法，在有限先验知识下实现激光雷达与相机间的交叉标定。我们利用在仓库采集的真实数据对该系统进行测试，结果验证了所提方法的有效性，并证明了为多类型传感器定制统一标定流程的可行性。
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