A Partition Filtering Method for 3D Sonar Point Cloud Data Considering Horizontal Deviation

Objectives: 3D sonar measurements are disturbed by the complex underwater environment and usually have a high level of noise, which requires fine filtering before they can be applied to underwater scenes. Methods: In this paper, a joint multi-feature hyperbolic voxel region blocking, local coordinate system construction, overall least squares local trend surface fitting, Grubbs test partition filtering considering the horizontal deviation of the point cloud data preprocessing method is proposed, and a set of point cloud denoising method applicable to threedimensional sonar point cloud is established. The method firstly realizes the fine division of underwater complex space with respect to the differences in normal vector, spatial distance and echo strength of point clouds in different regions; secondly, the trend surface fitting is carried out for local regions; finally, the multi-dimensional point cloud error detection data is constructed, and the super-voxels are divided into three types of regions based on the complexity of the terrain and the Grubbs test is used as a criterion of determination to realize the adaptive threshold denoising for the subregion. Results: The results show that the integrated filtering method proposed in this paper has good accuracy for both horizontal/vertical point cloud data, with an average overall accuracy of 99.3% and an average Kappa coefficient of 0.906 for the test results. Conclusions: The results show that the synthesized filtering method proposed in this paper has a significant improvement in accuracy compared with the traditional trend surface filtering method, and can be effectively applied to the filtering processing of three-dimensional sonar point cloud data in the underwater complex region.