Fusion Estimation of Point Sets from Multiple Stations of Spherical Coordinate Instruments Utilizing Uncertainty Estimation Based on Monte Carlo

Abstract

Multiple instrument stations, based on spherical coordinate measurements, are often used in the measurement of large objects. A data fusion method is proposed to derive optimal estimations of the positions of the object features, measured by more than one device. First, each device has a dedicated coordinate system that is linked together through the measurement of common points. Second, the weighted mean coordinates are derived. The covariance matrix of the sensory, covering of the radial distance and the angles, is propagated to get a weight matrix. Third, a nonlinear function is minimized to determine the optimized coordinate of the points. Monte Carlo error propagation is utilized to estimate the uncertainty of the fusion points. Simulation of the fusion algorithms is performed using laser tracking and laser radar. The fusion algorithm experiments are performed using two laser tracking stations. Simulation and experiments prove that the fusion method improves the precision of the measurements of an object’s location, due to incorporating the degree of uncertainty for each measurement point.