High-Performance 3D Point Cloud Image Distortion Calibration Filter Based on Decision Tree

Structured Light LiDAR is susceptible to lens scattering and temperature fluctuations, resulting in some level of distortion in the captured point cloud image. To address this problem, this paper proposes a high-performance 3D point cloud Least Mean Square filter based on Decision Tree, which is called the D−LMS filter for short. The D−LMS filter is an adaptive filtering compensation algorithm based on decision tree, which can effectively distinguish the signal region from the distorted region, thus optimizing the distortion of the point cloud image and improving the accuracy of the point cloud image. The experimental results clearly demonstrate that our proposed D−LMS filtering algorithm significantly improves accuracy by optimizing distorted areas. Compared with the 3D point cloud least mean square filter based on SVM, the accuracy of the proposed D−LMS filtering algorithm is improved from 86.17% to 92.38%, the training time is reduced by 1317 times and the testing time is reduced by 1208 times.