PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data

Abstract

Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.

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“…Another critical problem with generating finer images is the increase in processing complexity. A potential direction towards addressing the limitation of empty pixels would be applying filters, while k-nearest neighbor (kNN) can resolve the covered points issue [145,175]. These issues can appear in any approach that projects 3D points into 2D image representations.…”

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“…people, at the time of scanning. These points should be removed as much as possible before coregistering the individual scans to avoid confusion, especially when running automatic procedures (Cheng et al, 2021, Hang et al, 2017.…”

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“…The first two density clustering methods have problems in terms of the universality of steep terrain areas and are sensitive to the input parameters of the method. Before the application of the clustering method, principal component analysis (PCA) is usually used to represent the point set on the feature plane, and some noise points are removed by using the discrete nature of noise points [14,[24][25][26]. However, how to apply PCA in complex scenes is a problem.…”

“…The relationship between points or between point sets and point sets Statistical outlier removal (SOR) filter [18], [20] Spatial frequency outlier filter [19] Radius outlier removal filtering [12] Density-based spatial clustering application with noise (DBSCAN) [21][22][23] Principal component analysis [14], [24][25][26] This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication.…”