Data fusion strategies for reducing the uncertainty of point cloud data

For decades, contact probes on coordinate measuring machine (CMM) have been widely used for data acquisition in coordinate metrology, mainly because of their high accuracy. However, the acquired data is a low-density set of points, because sampling using a contact probe is a slow process. Nowadays, optical 3D scanning technologies such as structured-light scanners or laser scanners are increasingly included in metrology applications for rapidly capturing high-density point clouds enabling holistic measurements of a manufactured part. The recent 3D scanners can sample millions of coordinate data points from the part’s surface in just a few seconds. However, the accuracy of 3D scanners is about an order of magnitude lower than contact probes. The aim of this research project is to develop data fusion strategies for correcting the high-density 3D scanned point clouds using the low-density set of points measured by a contact probe, resulting in point clouds with reduced uncertainty.

Faculty Supervisor:

Farbod Khameneifar;René Mayer

Student:

Aria Ghazavi

Partner:

Pratt & Whitney Canada

Discipline:

Engineering - mechanical

Sector:

Aerospace and defense

University:

École Polytechnique de Montréal