An improved 2D LiDAR and camera fusion system is proposed for the 3D reconstruction of unknown environments. It combines the advantages of dense 2D point cloud and rich color image, adopting a differential evolutionary nonlinear tracking PID to control the pitching motion of LiDAR and camera accurately. The quadratic polynomial transition function is used to optimize the pitching trajectory. The environment was scanned by the system and converted into a 3D colored point cloud by the data fusion algorithm. The experimental results show: the proposed PID control algorithm can accurately control the pitching motion with a small average error (0.0267°) and significantly reduce the point cloud inhomogeneity (0.00698); the processing time for converting each 2D point cloud into the 3D point cloud is about 0.6 ms; combined with the data fusion algorithm, the system can obtain the dense colored 3D point cloud; compared with binocular camera, depth camera and 3D LiDAR under the condition of strong light interference, the fusion system outperforms, with the reconstruction object errors of distance, length and width of 0.23%, 0.17% and 0.46% respectively. In conclusion, the system can obtain homogeneous, and dense colored 3D point cloud in real time while ensuring stable refresh frame rate.

提出了一种改进的2D LiDAR和相机融合系统，用于未知环境的3D重建。它结合了2D点云密集和彩色图像丰富的优点，采用差分进化非线性跟踪PID来精确控制LiDAR和摄像机的俯仰运动。二次多项式过渡函数用于优化俯仰轨迹。系统对环境进行了扫描，并通过数据融合算法将其转换为3D彩色点云。实验结果表明：所提出的PID控制算法可以准确地控制俯仰运动，平均误差小（0.0267°），大大减少了点云的不均匀性（0.00698）。将每个2D点云转换为3D点云的处理时间约为0.6ms。结合数据融合算法，系统可以获得密集的彩色3D点云；与双目相机，深度相机和3D LiDAR相比，在强光干扰条件下，融合系统的性能要好，重建对象的距离，长度和宽度误差分别为0.23％，0.17％和0.46％。总而言之，该系统可以实时获取均匀且密集的彩色3D点云，同时确保稳定的刷新帧速率。