Sensor setups of robotic platforms commonly include both camera and LiDAR as they provide complementary information. However, fusing these two modalities typically requires a highly accurate calibration between them. In this paper, we propose MDPCalib which is a novel method for camera-LiDAR calibration that requires neither human supervision nor any specific target objects. Instead, we utilize sensor motion estimates from visual and LiDAR odometry as well as deep learning-based 2D-pixel-to-3D-point correspondences that are obtained without in-domain retraining. We represent the camera-LiDAR calibration as a graph optimization problem and minimize the costs induced by constraints from sensor motion and point correspondences. In extensive experiments, we demonstrate that our approach yields highly accurate extrinsic calibration parameters and is robust to random initialization. Additionally, our approach generalizes to a wide range of sensor setups, which we demonstrate by employing it on various robotic platforms including a self-driving perception car, a quadruped robot, and a UAV. To make our calibration method publicly accessible, we release the code on our project website at http://calibration.cs.uni-freiburg.de.

翻译：机器人平台的传感器配置常同时包含相机与激光雷达，二者提供互补感知信息。然而，融合这两种模态通常需要二者间的高精度标定。本文提出MDPCalib——一种无需人工监督或特定目标物体的相机-激光雷达标定新方法。该方法利用基于视觉与激光雷达里程计的传感器运动估计，以及无需域内重训练即可获得的深度学习二维像素-三维点对应关系。我们将相机-激光雷达标定建模为图优化问题，通过最小化传感器运动约束与点对应关系约束的联合代价函数进行求解。大量实验表明，本方法可生成高精度的外参标定参数，并对随机初始化具有鲁棒性。此外，该方法可泛化至多种传感器配置——我们在自动驾驶感知车、四足机器人及无人机等不同机器人平台上均验证了其有效性。为促进公开研究，我们已在项目网站http://calibration.cs.uni-freiburg.de 发布相关代码。