Study of weighted fusion methods for the measurement of surface geometry

•Four weighted fusion methods for surface measurement were classified and analysed.•The uncertainty propagation and the relationship with Kalman filter were analysed.•Advanced models compatible with different fusion methods were described.•Experiments verified the effectiveness of weighted fusion on accuracy improvement.•Weighted fusion complements residual approximation fusion in many fusion scenarios.

Four types of weighted fusion methods, including pixel-level, least-squares, parametrical and non-parametrical, have been classified and theoretically analysed in this study. In particular, the uncertainty propagation of the weighted least-squares fusion was analysed and its relation to the Kalman filter was studied. In cooperation with different fitting models, these four weighted fusion methods can be applied to a range of measurement challenges. The experimental results of this study show that the four weighted fusion methods compose a computationally efficient and reliable system for multi-sensor measurement problems, especially for freeform surface measurement. A comparison of weighted fusion with residual approximation-based fusion has also been conducted by providing the input datasets with different noise levels and sample sizes. The results demonstrated that weighted fusion and residual approximation-based fusion are complementary approaches applicable to most fusion scenarios.