A point cloud modeling method based on geometric constraints mixing the robust least squares method

2016 ◽  
Author(s):  
JIanping Yue ◽  
Yi Pan ◽  
Shun Yue ◽  
Dapeng Liu ◽  
Bin Liu ◽  
...  
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Least Squares Method ◽  
Modeling Method ◽  
Geometric Constraints ◽  
Robust Least Squares ◽  
Cloud Modeling

scholarly journals An Egg Volume Measurement System Based on the Microsoft Kinect

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2454 ◽  
Author(s):  
Ting Chan ◽  
Derek Lichti ◽  
Adam Jahraus ◽  
Hooman Esfandiari ◽  
Herve Lahamy ◽  
...  
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Least Squares Method ◽  
Point Clouds ◽  
Volume Estimation ◽  
Microsoft Kinect ◽  
Estimation Accuracy ◽  
Shape Parameters ◽  
Post Processing ◽  
Egg Volume

Measuring the volume of bird eggs is a very important task for the poultry industry and ornithological research due to the high revenue generated by the industry. In this paper, we describe a prototype of a new metrological system comprising a 3D range camera, Microsoft Kinect (Version 2) and a point cloud post-processing algorithm for the estimation of the egg volume. The system calculates the egg volume directly from the egg shape parameters estimated from the least-squares method in which the point clouds of eggs captured by the Kinect are fitted to novel geometric models of an egg in a 3D space. Using the models, the shape parameters of an egg are estimated along with the egg’s position and orientation simultaneously under the least-squares criterion. Four sets of experiments were performed to verify the functionality and the performance of the system, while volumes estimated from the conventional water displacement method and the point cloud captured by a survey-grade laser scanner serve as references. The results suggest that the method is straightforward, feasible and reliable with an average egg volume estimation accuracy 93.3% when compared to the reference volumes. As a prototype, the software part of the system was implemented in a post-processing mode. However, as the proposed processing techniques is computationally efficient, the prototype can be readily transformed into a real-time egg volume system.

scholarly journals POINT CLOUD SMOOTH SAMPLING AND SURFACE RECONSTRUCTION BASED ON MOVING LEAST SQUARES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 145-151 ◽  
Author(s):  
C. L. Kang ◽  
T. N. Lu ◽  
M. M. Zong ◽  
F. Wang ◽  
Y. Cheng
Keyword(s):  
Least Squares ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Least Squares Method ◽  
Moving Least Squares ◽  
Cloud Data ◽  
Grid Sampling ◽  
Moving Least Squares Method ◽  
Feature Information ◽  
Voxel Grid

Abstract. In point cloud data processing, smooth sampling and surface reconstruction are important aspects of point cloud data processing. In view of the current point cloud sampling method, the point cloud distribution is not uniform, the point cloud feature information is incomplete, and the reconstructed model surface is not smooth. This paper proposes a method of smoothing sampling processing and surface reconstruction using point cloud using moving least squares method. This paper first introduces the traditional moving least squares method in detail, and then proposes an improved moving least squares method for point cloud smooth sampling and surface reconstruction. In this paper, the algorithm is designed for the proposed theory, combined with C++ and point cloud library PCL programming, using voxel grid sampling and uniform sampling and moving least squares smooth sampling comparison, after sampling, using greedy triangulation algorithm surface reconstruction. The experimental results show that the improved moving least squares method performs point cloud smooth sampling more uniformly than the voxel grid sampling and the feature information is more prominent. The surface reconstructed by the moving least squares method is smooth, the surface reconstructed by the voxel grid sampling and the uniformly sampled data surface is rough, and the surface has a rough triangular surface. Point cloud smooth sampling and surface reconstruction based on moving least squares method can better maintain point cloud feature information and smooth model smoothness. The superiority and effectiveness of the method are demonstrated, which provides a reference for the subsequent study of point cloud sampling and surface reconstruction.

scholarly journals The Application of Robust Least Squares Method in Frequency Lock Loop Fusion for Global Navigation Satellite System Receivers

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1224 ◽  
Author(s):  
Mengyue Han ◽  
Qian Wang ◽  
Yuanlan Wen ◽  
Min He ◽  
Xiufeng He
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Satellite System ◽  
Complex Environment ◽  
Tracking Accuracy ◽  
Proposed Model ◽  
Robust Least Squares ◽  
Promising Application ◽  
Overall Performance ◽  
Global Navigation Satellite

The tracking accuracy of a traditional Frequency Lock Loop (FLL) decreases significantly in a complex environment, thus reducing the overall performance of a satellite receiver. In order to ensure high tracking accuracy of a receiver in a complex environment, this paper proposes a new tracking loop combining the vector FLL (VFLL) with a robust least squares method, which accurately matches the weights of received signals of different qualities to ensure high positioning accuracy. The weights of received signals are selected at the signal level, not at the observation level. In this paper, the ranges of strong and weak signals of the loop are determined according to the different expressions of the distribution function at different signal strengths, and the concept of loop segmentation is introduced. The segmentation results of the FLL are taken as a basis of the weight selection, and then combined with the Institute of Geodesy and Geophysics (IGGIII) weight function to obtain the equivalent weight matrix; the experiments are conducted to prove the advantages of the proposed method over the traditional methods. The experimental results show that the proposed VFLL tracking method has strong denoising capability under both normal- signal and harsh application environment conditions. Accordingly, the proposed model has a promising application perspective.

‘Least squares’ method—theorem or law?

1980 ◽  
Vol 59 (9) ◽  
pp. 8
Author(s):  
D.E. Turnbull
Keyword(s):  
Least Squares ◽  
Least Squares Method
Sign in / Sign up

Export Citation Format

Share Document