Abstract: A lens calibration system for calibrating a lens of an image capture device, the lens calibration system comprising: a processor configured to receive from the image capture device a series of image frames of a scene at different rotational positions of the image capture device; The processor being configured to: Identify in the series of image frames elements representing a first marker located at a first known distance from the image capture device and a second marker located at a second known distance from the image capture device, the first and the second known distances being different to one another; track the identified elements representing each marker across the series of image frames; process the tracked elements to determine a characteristic parameter of the lens of the image capture device; and build a lens model for the image capture device in dependence on the determined characteristic parameter of the lens.

Abstract: A method of estimating the navigational state of a system entails acquiring observation data produced by noisy measurement sensors and providing a probabilistic inference system to combine the observation data with prediction values of the system state space model to estimate the navigational state of the system. The probabilistic inference system is implemented to include a realization of a Gaussian approximate random variable propagation technique performing deterministic sampling without analytic derivative calculations. This technique achieves for the navigational state of the system an estimation accuracy that is greater than that achievable with an extended Kalman filter-based probabilistic inference system.