Paper L. Optimal Stochastic Linearization for Range-Based Localization. Edited version of the paper: F. Beutler, M. F. Huber, and U. D. Hanebeck. Optimal Stochastic Linearization for Range-Based Localization. In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5731-5736, Taipei, Taiwan, October 2010

In range-based localization, the trajectory of a mobile object is estimated based on noisy range measurements between the object and known landmarks. In order to deal with this uncertain information, a Bayesian state estimator is presented, which exploits optimal stochastic linearization. Compared to standard state estimators like the Extended or Unscented Kalman Filter, where a point-based Gaussian approximation is used, the proposed approach considers the entire Gaussian density for linearization. By employing the common assumption that the state and measurements are jointly Gaussian, the linearization can be calculated in closed formand thus analytic expressions for the range-based localization problem can be derived.