Laser scanners can be employed for spatial measuring tasks, but measuring accuracy is restricted because of the time of flight working principle. Laser-scanner-based observations with measuring errors might lead to rough spatial reconstruction. In this paper, an image registration method applying a Markov random field (MRF) algorithm is proposed. First, point cloud images are projected to a particular plane in a specific way. Then, the characteristic points of the projected image and the color image are extracted by an improved Harris algorithm. Next, the rotation and translation matrices can be calculated from the two image planes through the registration method. Finally, the MRF model is established describing the relation between the pixels and corresponding point cloud, which improves the resolution of the point cloud image. Furthermore, the color information of the point cloud is also matched. This method improves the efficiency and accuracy of registration. The final experimental result shows that using the MRF model increases measuring accuracy by 15%.

中文翻译：

---

激光扫描仪通过使用马尔可夫随机场算法改善了图像像素

可以将激光扫描仪用于空间测量任务，但是由于飞行时间的原则，测量精度受到限制。基于激光扫描仪的测量误差观测可能会导致粗糙的空间重建。本文提出了一种基于马尔可夫随机场（MRF）算法的图像配准方法。首先，将点云图像以特定方式投影到特定平面。然后，通过改进的Harris算法提取投影图像和彩色图像的特征点。接下来，可以通过配准方法从两个图像平面计算旋转和平移矩阵。最后，建立描述像素与对应点云之间关系的MRF模型，从而提高了点云图像的分辨率。此外，点云的颜色信息也匹配。此方法提高了注册效率和准确性。最终的实验结果表明，使用MRF模型可使测量精度提高15％。