Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping

Probabilistic robotics most often applied to the problem of simultaneous localisation and mapping (SLAM), requires measures of uncertainty to accompany observations of the environment. This paper describes how uncertainty can be characterised for a vision system that locates coloured landmarks in a typical laboratory environment. The paper describes a model of the uncertainty in segmentation, the internal cameral model and the mounting of the camera on the robot. It explains the implementation of the system on a laboratory robot, and provides experimental results that show the coherence of the uncertainty model.

Spectroscopic analysis of terrestrial analogues for the interpretation of surface composition of rocky bodies in the solar system. Effects of mineral mixing and particle size. Applications to moon, mars and implications for mercury.

Among the Solar System’s bodies, Moon, Mercury and Mars are at present, or have been in the recent years, object of space missions aimed, among other topics, also at improving our knowledge about surface composition. Between the techniques to detect planet’s mineralogical composition, both from remote and close range platforms, visible and near-infrared reflectance (VNIR) spectroscopy is a powerful tool, because crystal field absorption bands are related to particular transitional metals in well-defined crystal structures, e.g., Fe2+ in M1 and M2 sites of olivine or pyroxene (Burns, 1993). Thanks to the improvements in the spectrometers onboard the recent missions, a more detailed interpretation of the planetary surfaces can now be delineated. However, quantitative interpretation of planetary surface mineralogy could not always be a simple task. In fact, several factors such as the mineral chemistry, the presence of different minerals that absorb in a narrow spectral range, the regolith with a variable particle size range, the space weathering, the atmosphere composition etc., act in unpredictable ways on the reflectance spectra on a planetary surface (Serventi et al., 2014). One method for the interpretation of reflectance spectra of unknown materials involves the study of a number of spectra acquired in the laboratory under different conditions, such as different mineral abundances or different particle sizes, in order to derive empirical trends. This is the methodology that has been followed in this PhD thesis: the single factors previously listed have been analyzed, creating, in the laboratory, a set of terrestrial analogues with well-defined composition and size. The aim of this work is to provide new tools and criteria to improve the knowledge of the composition of planetary surfaces. In particular, mixtures composed with different content and chemistry of plagioclase and mafic minerals have been spectroscopically analyzed at different particle sizes and with different mineral relative percentages. The reflectance spectra of each mixture have been analyzed both qualitatively (using the software ORIGIN®) and quantitatively applying the Modified Gaussian Model (MGM, Sunshine et al., 1990) algorithm. In particular, the spectral parameter variations of each absorption band have been evaluated versus the volumetric FeO% content in the PL phase and versus the PL modal abundance. This delineated calibration curves of composition vs. spectral parameters and allow implementation of spectral libraries. Furthermore, the trends derived from terrestrial analogues here analyzed and from analogues in the literature have been applied for the interpretation of hyperspectral images of both plagioclase-rich (Moon) and plagioclase-poor (Mars) bodies.

Two Bayesian treatments of the n-tuple recognition method

Two probabilistic interpretations of the n-tuple recognition method are put forward in order to allow this technique to be analysed with the same Bayesian methods used in connection with other neural network models. Elementary demonstrations are then given of the use of maximum likelihood and maximum entropy methods for tuning the model parameters and assisting their interpretation. One of the models can be used to illustrate the significance of overlapping n-tuple samples with respect to correlations in the patterns.

Mixtures of probabilistic principal component analysers

Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a combination of local linear PCA projections. However, conventional PCA does not correspond to a probability density, and so there is no unique way to combine PCA models. Previous attempts to formulate mixture models for PCA have therefore to some extent been ad hoc. In this paper, PCA is formulated within a maximum-likelihood framework, based on a specific form of Gaussian latent variable model. This leads to a well-defined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context of clustering, density modelling and local dimensionality reduction, and we demonstrate its application to image compression and handwritten digit recognition.