Robust Estimation Method for Raw Observations from Laser Tracker

The triangulateration network adjustment method is a fundamental algorithm for constructing a measurement system composed of multiple laser trackers. This method is based on a prior-weight-matrix model considering varied accuracies of angle and distance observations from laser trackers, which can successfully reduce the influence of angle errors on the adjustment results. However, the precision of the measurement system based on the triangulateration network adjustment method is considerably reduced when gross errors are present in the observations using the least squares principle. This paper proposes a robust estimation method using raw observation data with different prior precision from laser trackers. First, the standard deviation of unit weight for each type of raw observation data is computed separately using the median function. Then, the raw observation data are reweighted using the Institute of Geodesy and Geophysics III (IGG3) equivalent weight function. Simulation and experimental results show that the proposed method achieves higher precision compared to the traditional robust estimation method when the observations contain data with different units and prior precisions.