Semi-Analytic Stochastic Linearization for Range-Based Pose Tracking

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

Optimal Gaussian Filtering for Polynomial Systems Applied to Association-free Multi-Target Tracking

This paper is about tracking multiple targets with the so-called Symmetric Measurement Equation (SME) filter. The SME filter uses symmetric functions, e.g., symmetric polynomials, in order to remove the data association uncertainty from the measurement equation. By this means, the data association problem is converted to a nonlinear state estimation problem. In this work, an efficient optimal Gaussian filter based on analytic moment calculation for discrete-time multi-dimensional polynomial systems corrupted with Gaussian noise is derived, and then applied to the polynomial system resulting from the SME filter. The performance of the new method is compared to an UKF implementation by means of typical multiple target tracking scenarios

Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems

Wireless Acoustic Tracking for Extended Range Telepresence

Telepresence systems enable a user to experience virtual or distant environments by providing sensory feedback. Appropriate devices include head mounted displays (HMD) for visual perception, headphones for auditory response, or even haptic displays for tactile sensation and force feedback. While most common designs use dedicated input devices like joysticks or a space mouse, the approach followed in the present work takes the user\u27s position and viewing direction as an input, as he walks freely in his local surroundings. This is achieved by using acoustic tracking, where the user\u27s pose (position and orientation) is estimated on the basis of ranges measured between a set of wall-fastened loudspeakers and a microphone array fixed on the user\u27s HMD. To allow for natural user motion, a wearable, fully wireless telepresence system is introduced. The increase in comfort compared to wired solutions is obvious, as the user\u27s awareness of distracting cables is taken away during walking. Also the lightweight design and small dimensions contribute to ergonomics, as the whole assembly fits well into a small backpack

Semi-analytic stochastic linearization for range-based pose tracking

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