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.