3D laser scanner calibration method based on invasive weed optimization and Levenberg-Marquardt algorithm

2017 ◽  
Author(s):  
Yuanyuan Li ◽  
Wanxu Zhang ◽  
Jianfu Cao ◽  
Jialin Peng ◽  
Bo Jiang ◽  
...  
Keyword(s):  
Laser Scanner ◽  
Calibration Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Scanner Calibration

scholarly journals HYBRID ONLINE MOBILE LASER SCANNER CALIBRATION THROUGH IMAGE ALIGNMENT BY MUTUAL INFORMATION

2016 ◽  
Vol III-1 ◽  
pp. 25-31 ◽  
Author(s):  
Mourad Miled ◽  
Bahman Soheilian ◽  
Emmanuel Habets ◽  
Bruno Vallet
Keyword(s):  
Mutual Information ◽  
Imaging System ◽  
Laser Scanner ◽  
Point Clouds ◽  
Calibration Method ◽  
Online Calibration ◽  
Calibration Methods ◽  
Laser Calibration ◽  
Scanner Calibration

This paper proposes an hybrid online calibration method for a laser scanner mounted on a mobile platform also equipped with an imaging system. The method relies on finding the calibration parameters that best align the acquired points cloud to the images. The quality of this intermodal alignment is measured by Mutual information between image luminance and points reflectance. The main advantage and motivation is ensuring pixel accurate alignment of images and point clouds acquired simultaneously, but it is also much more flexible than traditional laser calibration methods.

scholarly journals A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator

2020 ◽  
Vol 10 (20) ◽  
pp. 7320
Author(s):  
Phu-Nguyen Le ◽  
Hee-Jun Kang
Keyword(s):  
Neural Network ◽  
Robot Manipulator ◽  
Kinematic Model ◽  
Calibration Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Suggested Technique ◽  
Calibration Algorithm ◽  
Identification Technique ◽  
Position Precision

The study proposed a robotic calibration algorithm for improving the robot manipulator position precision. At first, the kinematic parameters as well as the compliance parameters of the robot can be identified together to improve its accuracy using the joint deflection model and the conventional kinematic model calibration technique. Then, an artificial neural network is constructed for further compensating the unmodeled errors. The invasive weed optimization is used to determine the parameters of the neural network. To show the advantages of the suggested technique, an HH800 robot is employed for the experimental study of the proposed algorithm. The improved position precision of the robot after the experiment firmly proves the practicability and positional precision of the proposed method over the other algorithms in comparison.

scholarly journals HYBRID ONLINE MOBILE LASER SCANNER CALIBRATION THROUGH IMAGE ALIGNMENT BY MUTUAL INFORMATION

2016 ◽  
Vol III-1 ◽  
pp. 25-31 ◽  
Author(s):  
Mourad Miled ◽  
Bahman Soheilian ◽  
Emmanuel Habets ◽  
Bruno Vallet
Keyword(s):  
Mutual Information ◽  
Imaging System ◽  
Laser Scanner ◽  
Point Clouds ◽  
Calibration Method ◽  
Online Calibration ◽  
Calibration Methods ◽  
Laser Calibration ◽  
Scanner Calibration

This paper proposes an hybrid online calibration method for a laser scanner mounted on a mobile platform also equipped with an imaging system. The method relies on finding the calibration parameters that best align the acquired points cloud to the images. The quality of this intermodal alignment is measured by Mutual information between image luminance and points reflectance. The main advantage and motivation is ensuring pixel accurate alignment of images and point clouds acquired simultaneously, but it is also much more flexible than traditional laser calibration methods.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

Multiple data parameter identification for nonlinear conceptual models

1997 ◽  
Vol 36 (5) ◽  
pp. 61-68 ◽  
Author(s):  
Hermann Eberl ◽  
Amar Khelil ◽  
Peter Wilderer
Keyword(s):  
Optimization Problem ◽  
Conceptual Models ◽  
Reference Data ◽  
Transport Model ◽  
Calibration Method ◽  
Data Sets ◽  
Multiple Data ◽  
Marquardt Algorithm ◽  
Multicriteria Optimization Problem ◽  
Higher Order Differential Equations

A numerical method for the identification of parameters of nonlinear higher order differential equations is presented, which is based on the Levenberg-Marquardt algorithm. The estimation of the parameters can be performed by using several reference data sets simultaneously. This leads to a multicriteria optimization problem, which will be treated by using the Pareto optimality concept. In this paper, the emphasis is put on the presentation of the calibration method. As an example identification of the parameters of a nonlinear hydrological transport model for urban runoff is included, but the method can be applied to other problems as well.

Sign in / Sign up

Export Citation Format

Share Document