In robotics, a key problem is for a robot to explore its environment and use the information gathered by its sensors to jointly produce a map of its environment, together with an estimate of its position: so-called SLAM (Simultaneous Localization and Mapping) [13]. Various filtering methods – Particle Filtering, and derived Kalman Filter methods (Extended, Unscented) – have been applied success...

A stochastic unscented Kalman filter is designed in an attempt to solve the state estimation problem of the greenhouse climate control systems with missing measurements. The missing measurements are described by a binary switching sequence satisfying a conditional probability distribution. In order to accommodate the effects of randomly varying arrival of measurement data, the stochastic unscen...

We propose a scalable multiple-output generalization of unscented and extended Gaussian processes. These algorithms have been designed to handle general likelihood models by linearizing them using a Taylor series or the Unscented Transform in a variational inference framework. We build upon random feature approximations of Gaussian process covariance functions and show that, on small-scale sing...

The localization of objects equipped with passive UHF RFID labels using the Received Signal Strength Indicator (RSSI) values is investigated. An Unscented Kalman Filter (UKF) is used for the localization of the object. Adding (N-1) additional tags with known relative position to the object (relative to its main tag), this information can be incorporated into the UKF resulting in an Unscented Ka...

In this short paper, we describe the tractography method based on the intrinsic unscented Kalman filer (IUKF) [1]. This method is a generalization of unscented Kalman filter (UKF) and involves the use of intrinsic geometry of the space of symmetric positive definite matrices denoted henceforth by Pn. In this filter, operations that are intrinsic to Pn are employed and thus no explicit constrain...

This paper describes the development of an approximate method for propagating uncertainty through stochastic dynamical systems using a quadrature rule integration based method. The development of quadrature rules for Gaussian mixture distributions is discussed. A numerical solution to this problem is considered that uses a Gram-Schmidt process. Simulation results are presented where the quadrat...

We introduce the Unscented von Mises–Fisher Filter (UvMFF), a nonlinear filtering algorithm for dynamic state estimation on the n-dimensional unit hypersphere. Estimation problems on the unit hypersphere occur in computer vision, for example when using omnidirectional cameras, as well as in signal processing. As approaches in literature are limited to very simple system and measurement models, ...

We develop a new approach for estimating the expected values of nonlinear functions applied to multivariate random variables with arbitrary distributions. Rather than assuming particular distribu...