For sensory data fusion, a calibration method between 3D light detection and ranging (LiDAR) and color camera based on ranging statistical characteristics and improved RANSAC algorithm is proposed. The multi-frame LiDAR point cloud data of the calibration triangular board are recorded. The scanned points with close angles are defined a cluster within same degrees. Furthermore, accurate points are preserved using statistical filtering based on Gaussian distribution. Afterwards, the plane and edge parameters of the triangular board are estimated by the reserved point cloud using improved the random sample consensus (RANSAC) algorithm to obtain the 3D locations of the vertices. Meanwhile, corner points in the image can be extracted manually. Finally, the projection matrix between the camera and the LiDAR is estimated by using the 2D–3D​ correspondences in different positions. The projection errors of different frames and corresponding points are calculated. The results demonstrate that the average error with 300 frames is reduced by 23.05% compared to 1 frame. Moreover, the standard deviation diminishes with the increasing of corresponding points. The reliability and advantage of the method are verified compared with other state-of-art methods. It provides theoretical and technical support for low resolution LiDAR.

针对感官数据融合，提出了一种基于测距统计特性和改进的RANSAC算法的3D光测距（LiDAR）与彩色相机之间的标定方法。记录校准三角板的多帧LiDAR点云数据。角度相近的扫描点被定义为同一角度内的簇。此外，使用基于高斯分布的统计过滤可保留准确的点。然后，使用改进的随机样本一致性（RANSAC）算法由保留点云估计三角形板的平面和边缘参数，以获得顶点的3D位置。同时，可以手动提取图像中的角点。最后，摄像机和LiDAR之间的投影矩阵是通过在不同位置使用2D–3D对应关系估算的。计算不同帧和对应点的投影误差。结果表明，与1帧相比，300帧的平均误差降低了23.05％。而且，标准偏差随着相应点的增加而减小。与其他最新方法相比，该方法的可靠性和优势得到了验证。它为低分辨率LiDAR提供理论和技术支持。标准偏差随着相应点的增加而减小。与其他最新方法相比，该方法的可靠性和优势得到了验证。它为低分辨率LiDAR提供理论和技术支持。标准偏差随着相应点的增加而减小。与其他最新方法相比，该方法的可靠性和优势得到了验证。它为低分辨率LiDAR提供理论和技术支持。