Paper M. Semi-Analytic Stochastic Linearization for Range-Based Pose Tracking. Edited version of the paper: F. Beutler, M. F. Huber, and U. D. Hanebeck. Semi-Analytic Stochastic Linearization for Range-Based Pose Tracking. In Proceedings of the 2010 IEEE International Conference onMultisensor Fusion and Integration for Intelligent Systems (MFI), pages 44-49, Salt Lake City, UT, USA, September 2010

In range-based pose tracking, the translation and rotation of an object with respect to a global coordinate system has to be estimated. The ranges are measured between the target and the global frame. In this paper, an intelligent decomposition is introduced in order to reduce the computational effort for pose tracking. Usually, decomposition procedures only exploit conditionally linear models. In this paper, this principle is generalized to conditionally integrable substructures and applied to pose tracking. Due to a modified measurement equation, parts of the problem can even be solved analytically.