The calibration of a 3D laser rangefinder (LRF) and a camera is a key technique in the field of computer vision and intelligent robots. This paper proposes a new method for the calibration of a 3D LRF and a camera based on optimization solution. The calibration is achieved by freely moving a checkerboard pattern in front of the camera and the 3D LRF. The images and the 3D point clouds of the checkerboard pattern in various poses are collected by the camera and the 3D LRF respectively. By using the images, the intrinsic parameters and the poses of the checkerboard pattern are obtained. Then, two kinds of geometric constraints, line-to-plane constraints and plane-to-plane constraints, are constructed to solve the extrinsic parameters by linear optimization. Finally, the intrinsic and extrinsic parameters are further refined by global optimization, and are used to compute the geometric mapping relationship between the 3D LRF and the camera. The proposed calibration method is evaluated with both synthetic data and real data. The experimental results show that the proposed calibration method is accurate and robust to noise.

中文翻译：

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基于优化解决方案的3D激光测距仪和摄像机的校准

3D激光测距仪（LRF）和相机的校准是计算机视觉和智能机器人领域的一项关键技术。本文提出了一种基于优化方案的3D LRF和摄像机标定的新方法。可以通过在相机和3D LRF前面自由移动棋盘格图案来实现校准。相机和3D LRF分别收集处于各种姿势的棋盘图案的图像和3D点云。通过使用图像，获得棋盘格图案的固有参数和姿势。然后，构造了两种几何约束：线对平面约束和平面对平面约束，以通过线性优化来求解外部参数。最后，通过全局优化进一步完善内在和外在参数，并用于计算3D LRF和相机之间的几何映射关系。利用合成数据和真实数据对所提出的校准方法进行评估。实验结果表明，所提出的校正方法准确，鲁棒。