A Gaussian process based data modelling and fusion method for multisensor coordinate measuring machines

Liu, Mingyu, Cheung, Benny and Li, Ze (2016) A Gaussian process based data modelling and fusion method for multisensor coordinate measuring machines. In: 31st Annual Meeting of the American Society for Precision Engineering, ASPE 2016.

A Gaussian process based data modelling and fusion method for multisensor coordinate measuring machines
Published manuscript
ASPE2016_ExAbstract-4727-final.pdf - Whole Document

Item Type:Conference or Workshop contribution (Poster)
Item Status:Live Archive


Multisensor measurement technology is an emerging technology which makes use of combinations of two or more different types of sensor probes so as to further enhance the measurement capability of the traditional single sensor coordinate measurement machines (CMMs). The sensors can complement each other’s limitations and improve the measurement accuracy. Nowadays, the applications of multisensor CMMs are becoming more and more widespread and many CMM manufacturers are developing multisensor CMMs in their advanced production lines. For instances, ZEISS O-INSPECT [1] equips with a contact sensor, imaging sensor and white light distance sensor, which is able to provide a fast inspection by the image sensor and high accuracy 3D measurement results by the contact sensor and white light distance sensor. Werth VideoCheck [2], is designed to equip with many kinds of sensors such as trigger probe, fiber probe and video sensor which provides the measurement ability of small features with the help of the small-diameter fiber probe in the scale down to 20 μm, as well as a quick checking with the fast trigger probe and image sensor. Hexagon Optiv Classic [3] provides a vision sensor and a tough trigger probe, while Nikon [4] enhances the true 3D multi-sensor measurement by combining vision sensor, laser auto-focus sensor, tactile sensor and rotary indexer. The measurement range, resolution and flexibility are largely enhanced by the complementary of the different characteristics of various sensors. The combination of different types of sensors extends the measurement ability such as accuracy and measurement range of the CMMs. However, most of the multisensor CMMs are lack of an optimal strategy to perform multisensor measurement and fusion of data from different sensors. Some of the studies for multisensor CMM focused on complementary measurement for special geometrical features. Nashman et al. [5] used a camera sensor to locate and measure the feature such as object edges, corners and centroids while the touch sensor was used to measure other part of the object. The touch sensor was highly accurate with little noise. However, it could not measure sharp features such as edges and corners. Combining these two sensors enable the capability to gather high bandwidth global information and to obtain high accurate measurement information. Zexiao et al. [6] used a multi-probe system which consists of a structure light sensor and a trigger probe to measure multiple features including edges. However, the edges were not directly measured while they were generated by fitting the surfaces using the measured points on the relatively smooth surfaces instead. This paper presents a Gaussian process based data modelling and data fusion (GP-DMF) method which first estimates the mean surfaces and uncertainties of the datasets obtained from different sensors and combines the two measurement data into a single one with associated uncertainty. A series of simulation and measurement experiments have been conducted to verify the technical feasibility of the method. The results show that the fused data with a lower uncertainty are obtained. The proposed GP-DMF method attempts to provide a generalized data-orientation multi-sensor measurement method which does not rely on the sensor itself and this makes it having potential to be used in a wide application fields.

Keywords:GAUSSIAN PROCESS, data fusion, coordinate measuring machines
Subjects:H Engineering > H700 Production and Manufacturing Engineering
Divisions:College of Science > School of Engineering
ID Code:53931
Deposited On:11 Jul 2023 10:51

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