scholarly journals A Point Clouds Filtering Algorithm Based on Grid Partition and Moving Least Squares

2012 ◽  
Vol 28 ◽  
pp. 476-482 ◽  
Author(s):  
Liu Changjun ◽  
Li Jian ◽  
Zhang Shunfu ◽  
Ding Liuqian
Keyword(s):  
Least Squares ◽  
Point Clouds ◽  
Moving Least Squares ◽  
Filtering Algorithm ◽  
Grid Partition

Adaptive Moving Least-Squares Surfaces for Multiple Point Clouds Registration

2011 ◽  
Author(s):  
Yunbao Huang ◽  
Linchi Zhang ◽  
Zhihui Tan ◽  
Qifu Wang ◽  
Liping Chen
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Synthetic Data ◽  
Point Clouds ◽  
Integral Invariant ◽  
Gaussian Kernel ◽  
Moving Least Squares ◽  
Multiple Point ◽  
Surface Model ◽  
Core Idea

In this paper, we propose an Adaptive Moving Least-Squares (AMLS) surface based approach for multi-view or multi-sensor point cloud ICP registration. The core idea of this approach is to reconstruct a smooth and accurate surface, e. s. AMLS surface, from a point cloud, without data segmentation and surface model selection, resulting in an accurate point-to-AMLS surface ICP registration. The major difference between AMLS and traditional MLS is that the width of Gaussian kernel is adaptively scaled with the principle curvature, which is defined through local integral invariant analysis. Experimental results of both synthetic data and scanned data from a mechanical part show that the presented approach is more accurate and robust on sensor noise and sample density.

scholarly journals Using Least-Squares Residuals to Assess the Stochasticity of Measurements—Example: Terrestrial Laser Scanner and Surface Modeling

2021 ◽  
Vol 5 (1) ◽  
pp. 59
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Least Squares ◽  
Laser Scanner ◽  
Real Data ◽  
Point Clouds ◽  
Statistical Testing ◽  
Normal Vector ◽  
Engineering Structures ◽  
3D Point Clouds ◽  
Least Squares Adjustment ◽  
Correlation Parameters

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolution mapping of terrain. The noise of the observations cannot be assumed to be strictly corresponding to white noise: besides being heteroscedastic, correlations between observations are likely to appear due to the high scanning rate. Unfortunately, if the variance can sometimes be modeled based on physical or empirical considerations, the latter are more often neglected. Trustworthy knowledge is, however, mandatory to avoid the overestimation of the precision of the point cloud and, potentially, the non-detection of deformation between scans recorded at different epochs using statistical testing strategies. The TLS point clouds can be approximated with parametric surfaces, such as planes, using the Gauss–Helmert model, or the newly introduced T-splines surfaces. In both cases, the goal is to minimize the squared distance between the observations and the approximated surfaces in order to estimate parameters, such as normal vector or control points. In this contribution, we will show how the residuals of the surface approximation can be used to derive the correlation structure of the noise of the observations. We will estimate the correlation parameters using the Whittle maximum likelihood and use comparable simulations and real data to validate our methodology. Using the least-squares adjustment as a “filter of the geometry” paves the way for the determination of a correlation model for many sensors recording 3D point clouds.

Modelling the characteristics of turbocompressors for fuel cell systems using hybrid method based on moving least squares

2009 ◽  
Vol 86 (7-8) ◽  
pp. 1283-1289 ◽  
Author(s):  
R. Tirnovan ◽  
S. Giurgea ◽  
A. Miraoui ◽  
M. Cirrincione
Keyword(s):  
Fuel Cell ◽  
Least Squares ◽  
Hybrid Method ◽  
Moving Least Squares ◽  
Cell Systems
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