Error compensation for 3D laser scanning system based on neural network

Author

Li, Peng ; Hu, Ying ; Chen, Tianfei

Author_Institution

Autom. Res. Centre, Dalian Maritime Univ., Dalian, China

fYear

2012

fDate

23-25 May 2012

Firstpage

3875

Lastpage

3878

Abstract

In the field of reverse engineering, high-precision point cloud data is a guarantee of quality and accuracy for three-dimensional (3D) reconstruction model. Aiming to decrease the measuring error of the laser scanning system, an error compensation method for the original point cloud data is proposed. Firstly, the data error is obtained through the comparison between CAD model and original data. Taking the original point cloud data and error data as learning samples, the training work for the BP network is then completed, and the error compensation model is established. Finally, the reliability of the error compensation model is verified by the samples of testing data. With the help of BP compensation model, any original point data error from the laser scanning system can be compensated. And, the results of experiments show the practicality of the method.

Keywords

CAD/CAM; backpropagation; computerised instrumentation; data handling; error compensation; learning (artificial intelligence); measurement systems; neural nets; precision engineering; reverse engineering; 3D laser scanning system; BP compensation model; BP network; CAD model; error compensation method; error compensation model; error compensation model reliability; high-precision point cloud data error; neural network; reverse engineering; testing data; three-dimensional reconstruction model; training work; Data models; Design automation; Error compensation; Laser modes; Neural networks; Solid modeling; Training; BP network; Error compensation; Point cloud error; Reverse engineering;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2012 24th Chinese

Conference_Location

Taiyuan

Print_ISBN

978-1-4577-2073-4

Type

conf

DOI

10.1109/CCDC.2012.6243101

Filename

6243101