3D-point-cloud registration and real-world dynamic modelling-based virtual environment building method for teleoperation

SUMMARYIt is a challenging task for a human operator to manipulate a robot from a remote distance, especially in an unknown environment. Excellent teleoperation provides the human operator with a sense of telepresence, mainly including real-world vision, haptic perception, etc. This paper presents a novel virtual environment building method using the red–green–blue (RGB) colour information, the surface normal feature-based 3D-point-cloud registration method and the weighted sliding-average least-square-method-based real-world dynamic modelling for teleoperation. The experiments prove the method to be an accurate and effective means of teleoperation.

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3D-point-cloud registration and real-world dynamic modelling-based virtual environment building method for teleoperation- CORRIGENDUM

Robotica ◽

10.1017/s0263574717000327 ◽

2017 ◽

Vol 36 (2) ◽

pp. 312-312

Author(s):

Dejing Ni ◽

Aiguo Song ◽

Xiaonong Xu ◽

Huijun Li ◽

Chengcheng Zhu ◽

...

Keyword(s):

Virtual Environment ◽

Real World ◽

Point Cloud ◽

Dynamic Modelling ◽

Original Version ◽

3D Point Cloud ◽

Point Cloud Registration ◽

Image Position

The Figure 4 in the original version has a stylistic error.It ought to use the figure as follows: The authors apologise for this error.

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Point Cloud Registration Method Based on Face-Mating after Denosing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.131 ◽

2014 ◽

Vol 536-537 ◽

pp. 131-135 ◽

Cited By ~ 2

Author(s):

Tian Fan Chen ◽

Cheng Hui Gao ◽

Bing Wei He

Keyword(s):

Least Squares ◽

Point Cloud ◽

Convergence Speed ◽

Least Square ◽

Point Cloud Registration ◽

Registration Method ◽

Least Squares Fitting ◽

Noise Point ◽

Coarse Registration

A method is presented to accurate face-mating point cloud registration after dealing with noise point. Point cloud registration is divided into two parts,firstly,coarse registration is applied for visual point cloud,then three local sufaces of overlap cloud region are selected to be mating calculated after denosing base on least squares fitting , at last accurate splicing parameters of translation and rotation are acquired by nonlinear least square .This algorithm is easy to deal with the denosing, has faster convergence speed and higher registration accuracy.Its feasibility is proved by samples.

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Fast Method of Registration for 3D RGB Point Cloud with Improved Four Initial Point Pairs Algorithm

Sensors ◽

10.3390/s20010138 ◽

2019 ◽

Vol 20 (1) ◽

pp. 138 ◽

Cited By ~ 1

Author(s):

Peng Li ◽

Ruisheng Wang ◽

Yanxia Wang ◽

Ge Gao

Keyword(s):

Point Cloud ◽

Three Dimensional ◽

Initial Point ◽

Least Square ◽

Color Filter ◽

Fast Method ◽

3D Point Cloud ◽

Point Cloud Registration ◽

Global Registration ◽

Rgb Values

Three-dimensional (3D) point cloud registration is an important step in three-dimensional (3D) model reconstruction or 3D mapping. Currently, there are many methods for point cloud registration, but these methods are not able to simultaneously solve the problem of both efficiency and precision. We propose a fast method of global registration, which is based on RGB (Red, Green, Blue) value by using the four initial point pairs (FIPP) algorithm. First, the number of different RGB values of points in a dataset are counted and the colors in the target dataset having too few points are discarded by using a color filter. A candidate point set in the source dataset are then generated by comparing the similarity of colors between two datasets with color tolerance, and four point pairs are searched from the two datasets by using an improved FIPP algorithm. Finally, a rigid transformation matrix of global registration is calculated with total least square (TLS) and local registration with the iterative closest point (ICP) algorithm. The proposed method (RGB-FIPP) has been validated with two types of data, and the results show that it can effectively improve the speed of 3D point cloud registration while maintaining high accuracy. The method is suitable for points with RGB values.

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A Fast and Robust Rotation Search and Point Cloud Registration Method for 2D Stitching and 3D Object Localization

Applied Sciences ◽

10.3390/app11209775 ◽

2021 ◽

Vol 11 (20) ◽

pp. 9775

Author(s):

Lei Sun ◽

Zhongliang Deng

Keyword(s):

Real World ◽

Point Cloud ◽

Point Clouds ◽

Object Localization ◽

Point Cloud Registration ◽

Registration Method ◽

3D Object ◽

Real World Application ◽

Large Numbers ◽

Optimal Rotation

Rotation search and point cloud registration are two fundamental problems in robotics, geometric vision, and remote sensing, which aim to estimate the rotation and transformation between the 3D vector sets and point clouds, respectively. Due to the presence of outliers (probably in very large numbers) among the putative vector or point correspondences in real-world applications, robust estimation is of great importance. In this paper, we present Inlier searching using COmpatible Structures (ICOS), a novel, efficient, and highly robust solver for both the correspondence-based rotation search and point cloud registration problems. Specifically, we (i) propose and construct a series of compatible structures for the two problems, based on which various invariants can be established, and (ii) design time-efficient frameworks to filter out outliers and seek inliers from the invariant-constrained random sampling based on the compatible structures proposed. In this manner, even with extreme outlier ratios, inliers can be effectively sifted out and collected for solving the optimal rotation and transformation, leading to our robust solver ICOS. Through plentiful experiments over standard datasets, we demonstrated that: (i) our solver ICOS is fast, accurate, and robust against over 95% outliers with nearly a 100% recall ratio of inliers for rotation search and both known-scale and unknown-scale registration, outperforming other state-of-the-art methods, and (ii) ICOS is practical for use in real-world application problems including 2D image stitching and 3D object localization.

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