Abstract: Aiming at the problem that point cloud overfitting in point cloud segmentation in low light and low feature point environment,this paper proposes a GS-SVM point cloud segmentation algorithm that based on polynomial kernel least squares support vector machine and grid search. It's extracted to classify multi-dimensional features such as vehicles,lane lines and garage obstacle pillars. The multi-scale accuracy evaluation index is used to verify the effectiveness of feature selection and evaluate the segmentation algorithm. The results show that compared with the traditional classification algorithm in the literature,the column recognition rate is 80% and the vehicle recognition rate is 65%. The classification algorithm based on polynomial kernel function has a recognition rate of 75% and 73% for columns and vehicles,respectively,which is increased by 5% and 8%. When using the other two kernel function,GS-SVM also maintains the advantages,comparing with the conventional algorithm,the proposed algorithm has strong robustness,which provides a solution to the problem of point cloud segmentation in weak light and geographical feature points environment. It also enriches the use scene of LiDAR 3D scanning.

Key words: point cloud segmentation, machine learning, GS-SVM, SMRF filtering algorithm, laser LiDAR scan