Contour Matching-Based Nondiffuse Reflection Noise Point Cloud Detection Method

To mitigate the impact of non-diffuse reflection noise in light detection and ranging (LiDAR) point clouds on the mapping accuracy of simultaneous localization and mapping (SLAM) technology, this paper proposes a contour-matching-based method for detecting non-diffuse reflection noise in point clouds. First, based on the clustering segmentation results of a single-frame point cloud, all independent point cloud clusters are projected onto the X–Z and Y–Z planes. Then, the Alpha Shape (AS) algorithm is applied to extract the two-dimensional contours of each projected point cloud cluster, and corresponding feature vectors are constructed based on the geometric properties of these contours. Finally, by leveraging the symmetry properties of non-diffuse reflection point cloud clusters, non-diffuse reflection noise points are identified through the computation of cosine similarity (CS) between feature vectors. Experimental results indicate that the proposed method achieves a correct removal rate of 91.44% for non-diffuse noise points and a false removal rate of 5.77% for non-noise points. Compared with similar approaches, the correct detection rate of non-diffuse noise points is improved by 2.71%, while the false detection rate of non-noise points is reduced by 1.49%. Each point cloud frame is processed in 0.0741 seconds. The proposed approach effectively eliminates non-diffuse reflection noise points in point cloud data, mitigating the adverse impact of non-diffuse objects on LiDAR data quality.