An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells

Abstract

Plane segmentation is a basic task in the automatic reconstruction of indoor and urban environments from unorganized point clouds acquired by laser scanners. As one of the most common plane-segmentation methods, standard Random Sample Consensus (RANSAC) is often used to continually detect planes one after another. However, it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points. An improved RANSAC method based on Normal Distribution Transformation (NDT) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation. A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface. The 3D NDT represents the point cloud with a set of NDT cells and models the observed points with a normal distribution within each cell. The geometric appearances of NDT cells are used to classify the NDT cells into planar and non-planar cells. The proposed method is verified on three indoor scenes. The experimental results show that the correctness exceeds 88.5% and the completeness exceeds 85.0%, which indicates that the proposed method identifies more reliable and accurate planes than standard RANSAC. It also executes faster. These results validate the suitability of the method.

Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation

“…The identification is split into four steps: (one-slicing) the points are sliced into a set of pieces; (two-line extraction) a region-growing line extraction method and an IRLS line fitting algorithm are proposed to extract a segment hypothesis that represents the wall direction by extending a previous work [46]; (three-line projection & fusion) the extracted segments are projected into the horizontal plane and merged by a line fusion algorithm; and, (four-cell decomposition) the plane spaces are partitioned into a two-dimensional (2D) cell decomposition by extended lines from extracted segments. …”

“…Then, an IRLS algorithm [46] that uses the M-estimator is proposed for line fitting in each separated region. For a point cloud = { … … } ∈ , the line-fitting problem can be considered to fit points to a line.…”

“…The weakness of Schnabel's algorithm is its highly variable point density and strong anisotropy [1]. Furthermore, this algorithm is not robust on outliers and noise points that are caused by the uncertainty of randomly sampling the minimum subset with three points [2,46].…”

Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

“…Segmentation techniques can be focused on different strategies such as iterative search of basic primitives (planes, lines, etc.) [13][14][15][16][17]; evaluation of different sets of characteristics calculated from a point and its neighborhood [18][19][20]; or multiclass classification techniques based on supervised machine learning algorithms [21][22][23]. In this way, it is possible to locate different elements regardless the complexity of the scenario going from simple geometries such as roofs [24] or columns [25] to complex geometries such as trees [26,27], buildings [24,28,29] or vehicles [30].…”

A Robust Transform Estimator Based on Residual Analysis and Its Application on UAV Aerial Images

“…This process is called matched feature refinement, where the correct and the wrong matches are denoted as inliers and outliers, respectively. As for the transform estimation algorithm, Random Sample Consensus (RANSAC) [13] is one of the most famous that has been widely used in remote sensing area, such as registration [14], robot localization [15] and plane fitting in point clouds [16]. Typically, RANSAC for UAV-based image transform estimation algorithm includes the following three key steps.…”