Abstract
This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the Gaussian process is used as mathematical foundation to establish an inferring plausible model to aid the measurement process via multi-feature classification and multi-dataset regression. Multi-feature classification extracts and classifies the geometric features of the measured surfaces at different scales to design an appropriate composite covariance kernel and corresponding initial sampling strategy. Multi-dataset regression takes the designed covariance kernel as input to fuse the multi-sensor measured datasets with Gaussian process model, which is further used to adaptively refine the initial sampling strategy by taking the credibility of the fused model as the critical sampling criteria. Hence, intelligent sampling can be realized with consecutive learning process with full Bayesian treatment. The statistical nature of the Gaussian process model combined with various powerful covariance kernel functions offer the system great flexibility for different kinds of complex surfaces.
Highlights
Surface size, geometry and texture are some of the most influential subjects in the field of precision engineering [1]
There are a large amount of literature on this topic [22,23,24], the surface modelling in multi-sensor system is still a challenging task since the measured datasets are normally embedded in different coordinate frames with different resolutions and different levels of uncertainties
The proposed Gaussian process (GP)-BIS has applied to an instrument equipped with a touch trigger probe and a laser scanner
Summary
Geometry and texture are some of the most influential subjects in the field of precision engineering [1]. There are a large amount of literature on this topic [22,23,24], the surface modelling in multi-sensor system is still a challenging task since the measured datasets are normally embedded in different coordinate frames with different resolutions and different levels of uncertainties This requires the surface modelling methods be capable of efficiently fusing multi-sensor measured datasets and hopefully be capable of performing self-assessment so as to give some hints to the sample distribution [25]. The statistic nature of GP makes it capable of incorporating the measurement errors into the modelling process and assigning credibility to the constructed model Taking these advantages, this paper presents a GP based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor system.
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