In many fields of robotics, knowing the relative position and orientation between two sensors is a mandatory precondition to operate with multiple sensing modalities. In this context, the pair LiDAR-RGB cameras offer complementary features: LiDARs yield sparse high quality range measurements, while RGB cameras provide a dense color measurement of the environment. Existing techniques often rely either on complex calibration targets that are expensive to obtain, or extracted virtual correspondences that can hinder the estimate's accuracy. In this paper we address the problem of LiDAR-RGB calibration using typical calibration patterns (i.e. A3 chessboard) with minimal human intervention. Our approach exploits the planarity of the target to find correspondences between the sensors measurements, leading to features that are robust to LiDAR noise. Moreover, we estimate a solution by solving a joint non-linear optimization problem. We validated our approach by carrying on quantitative and comparative experiments with other state-of-the-art approaches. Our results show that our simple schema performs on par or better than other approches using complex calibration targets. Finally, we release an open-source C++ implementation at \url{https://github.com/srrg-sapienza/ca2lib}

翻译：在机器人学的许多领域中，了解两个传感器之间的相对位置和姿态是使用多种传感模态进行操作的强制性前提。在此背景下，LiDAR与RGB相机这对传感器提供互补特性：LiDAR可获取稀疏的高质量距离测量值，而RGB相机则提供环境的稠密色彩测量值。现有技术通常依赖于成本高昂的复杂标定目标，或利用提取的虚拟对应点，但这些虚拟对应点可能影响估计的精度。本文提出一种使用典型标定图案（即A3棋盘格）且只需最少人工干预的LiDAR-RGB标定方法。我们的方法利用目标的平面性来建立传感器测量值之间的对应关系，从而获得对LiDAR噪声具有鲁棒性的特征。此外，我们通过求解一个联合非线性优化问题来估计标定解。通过与其他前沿方法进行定量和对比实验，验证了我们的方法的有效性。结果表明，我们提出的简单方案在性能上可与使用复杂标定目标的方法媲美甚至更优。最后，我们在 \url{https://github.com/srrg-sapienza/ca2lib} 上开源了一个C++实现。