Non-uniform 3D light detection and ranging (LiDAR) point-cloud data with outlier noises are not conducive to interframe point-cloud-matching in urban environments. Thus, an outlier noise filtering algorithm for large-scale scattered LiDAR point-cloud in urban environments is proposed. This algorithm improves the traditional density-based spatial clustering of applications with noise (DBSCAN) algorithm by applying voxel-grid partitioning to the three-dimensional point-cloud to create a set of grid cells, which greatly reduces the search scope of each object's neighborhood in the data-space range. The improved algorithm can quickly find each cluster, which separates the target point-cloud from the outliers, thus eliminating the outlier noise in the point-cloud. The experimental results show that the proposed algorithm can process point-cloud data in real-time, ensure three-dimensional geometric features of point-cloud, effectively recognize and filter out outlier noise, reduce the scale of point-cloud, and speed up the subsequent processing efficiency of the point-cloud.Using this algorithm, the accuracy of matching between the frames is doubled, and the matching time is only one-third of the time before denoising.