The common state filter for SLAM

This paper presents the Common State Filter (CSF), a novel and efficient suboptimal Multiple Hypothesis SLAM (MHSLAM) method for Kalman Filter-based SLAM algorithms. Conventional MHSLAM algorithms require the entire vehicle and map state to be copied for each hypothesis. The CSF, by contrast, maintains a single, common instance of the vast majority of the map and only copies the map portion that varies substantially across different hypotheses. We demonstrate the performance of the algorithm on the Victoria Park data set. ©2008 IEEE

The importance of screening in children who snore

It is important to screen for OSAS in children who snore, as early treatment of OSAS can prevent neurocognitive, behavioural, cardiovascular and metabolic consequences. Paediatricians should always investigate sleep habits and the possible presence of snoring, respiratory efforts or pauses during routine examination of children. These instruments may be effectively used to identify patients with OSAS, and the specificity and positive predictive value may be increased by adding other screening instruments such as nocturnal pulse oximetry [10]. The sleep questionnaires are instruments that can be used to screen patient candidates for a PSG study for suspected OSAS, and to identify those with a mild form of SDB, enabling early treatment

A Probabilistic Perspective on Gaussian Filtering and Smoothing

We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and smoothers can be derived straightforwardly by providing methods for computing these moments. Based on this insight, we derive the cubature Kalman smoother and propose a novel robust filtering and smoothing algorithm based on Gibbs sampling

Measuring strain at the atomic-scale with Differential X-ray Absorption Spectroscopy

Strain-inducing phenomena, such as magnetostriction, lie at the heart of transducer technologies. Knowledge of their origin and mechanics, and how they manifest themselves in different materials, underpins the development and optimisation of sensor and actuator devices. DiffXAS has been developed to permit strain measurements at an atomic-scale, and thus verify theoretical models for transducer behaviour.Submitted versio

Avoiding negative depth in inverse depth bearing-only SLAM

In this paper we consider ways to alleviate negative estimated depth for the inverse depth parameterisation of bearing-only SLAM. This problem, which can arise even if the beacons are far from the platform, can cause catastrophic failure of the filter.We consider three strategies to overcome this difficulty: applying inequality constraints, the use of truncated second order filters, and a reparameterisation using the negative logarithm of depth. We show that both a simple inequality method and the use of truncated second order filters are succesful. However, the most robust peformance is achieved using the negative log parameterisation. ©2008 IEEE

PILCO: A Model-Based and Data-Efficient Approach to Policy Search

In this paper, we introduce PILCO, a practical, data-efficient model-based policy search method. PILCO reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way. By learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning, PILCO can cope with very little data and facilitates learning from scratch in only a few trials. Policy evaluation is performed in closed form using state-of-the-art approximate inference. Furthermore, policy gradients are computed analytically for policy improvement. We report unprecedented learning efficiency on challenging and high-dimensional control tasks. Copyright 2011 by the author(s)/owner(s)