How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change

Direct visual localization has recently enjoyed a resurgence in popularity with the increasing availability of cheap mobile computing power. The competitive accuracy and robustness of these algorithms compared to state-of-the-art feature-based methods, as well as their natural ability to yield dense maps, makes them an appealing choice for a variety of mobile robotics applications. However, direct methods remain brittle in the face of appearance change due to their underlying assumption of photometric consistency, which is commonly violated in practice. In this paper, we propose to mitigate this problem by training deep convolutional encoder-decoder models to transform images of a scene such that they correspond to a previously-seen canonical appearance. We validate our method in multiple environments and illumination conditions using high-fidelity synthetic RGB-D datasets, and integrate the trained models into a direct visual localization pipeline, yielding improvements in visual odometry (VO) accuracy through time-varying illumination conditions, as well as improved metric relocalization performance under illumination change, where conventional methods normally fail. We further provide a preliminary investigation of transfer learning from synthetic to real environments in a localization context. An open-source implementation of our method using PyTorch is available at https://github.com/utiasSTARS/cat-net.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE International Conference on Robotics and Automation (ICRA'18), Brisbane, Australia, May 21-25, 201

Incremental Adversarial Domain Adaptation for Continually Changing Environments

Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics applications exhibit these conditions and thus facilitate the potential to incrementally adapt a learnt model over minor shifts which integrate to massive differences over time. Our work presents an adversarial approach for lifelong, incremental domain adaptation which benefits from unsupervised alignment to a series of intermediate domains which successively diverge from the labelled source domain. We empirically demonstrate that our incremental approach improves handling of large appearance changes, e.g. day to night, on a traversable-path segmentation task compared with a direct, single alignment step approach. Furthermore, by approximating the feature distribution for the source domain with a generative adversarial network, the deployment module can be rendered fully independent of retaining potentially large amounts of the related source training data for only a minor reduction in performance.Comment: International Conference on Robotics and Automation 201

Length scales, collective modes, and type-1.5 regimes in three-band superconductors

The recent discovery of iron pnictide superconductors has resulted in a rapidly growing interest in multiband models with more than two bands. In this work we specifically focus on the properties of three-band Ginzburg-Landau models which do not have direct counterparts in more studied two-band models. First we derive normal modes and characteristic length scales in the conventional U(1) three-band Ginzburg-Landau model as well as in its time reversal symmetry broken counterpart with $U(1)\times Z_2$ symmetry. We show that in the latter case, the normal modes are mixed phase/density collective excitations. A possibility of the appearance of a massless phase-difference mode associated with fluctuations of the phase difference is also discussed. Next we show that gradients of densities and phase differences can be inextricably intertwined in vortex excitations in three-band models. This can lead to very long-range attractive intervortex interactions and appearance of type-1.5 regimes even when the intercomponent Josephson coupling is large. In some cases it also results in the formation of a domain-like structures in the form of a ring of suppressed density around a vortex across which one of the phases shifts by $\pi$. We also show that field-induced vortices can lead to a change of broken symmetry from U(1) to $U(1)\times Z_2$ in the system. In the type-1.5 regime, it results in a semi-Meissner state where the system has a macroscopic phase separation in domains with broken U(1) and $U(1)\times Z_2$ symmetries.Comment: Version 3: Corrected som inconstancies in the parameter set in Fig.2 Also som minor typos corrected. No changes to results or conclusion

A new fit to solar neutrino data in models with large extra dimensions

String inspired models with millimeter scale extra dimensions provide a natural way to understand an ultralight sterile neutrino needed for a simultaneous explanation of the solar, atmospheric and LSND neutrino oscillation results. The sterile neutrino is the bulk neutrino ($\nu_B$) postulated to exist in these models, and it becomes ultralight in theories that prevent the appearance of its direct mass terms. Its Kaluza-Klein (KK) states then add new oscillation channels for the electron neutrino emitted from the solar core. We show that successive MSW transitions of solar $\nu_e$ to the lower lying KK modes of $\nu_B$ in conjunction with vacuum oscillations between the $\nu_e$ and the zero mode of $\nu_B$ provide a new way to fit the solar neutrino data. Using just the average rates from the three types of solar experiments, we predict the Super-Kamiokande spectrum with 73\% probability, but dips characteristic of the 0.06 mm extra dimension should be seen in the SNO spectrum. We discuss both intermediate and low string scale models where the desired phenomenology can emerge naturally.Comment: 20 pages, contains updated SuperK results and reference

A Unified Framework for Compositional Fitting of Active Appearance Models

Active Appearance Models (AAMs) are one of the most popular and well-established techniques for modeling deformable objects in computer vision. In this paper, we study the problem of fitting AAMs using Compositional Gradient Descent (CGD) algorithms. We present a unified and complete view of these algorithms and classify them with respect to three main characteristics: i) cost function; ii) type of composition; and iii) optimization method. Furthermore, we extend the previous view by: a) proposing a novel Bayesian cost function that can be interpreted as a general probabilistic formulation of the well-known project-out loss; b) introducing two new types of composition, asymmetric and bidirectional, that combine the gradients of both image and appearance model to derive better conver- gent and more robust CGD algorithms; and c) providing new valuable insights into existent CGD algorithms by reinterpreting them as direct applications of the Schur complement and the Wiberg method. Finally, in order to encourage open research and facilitate future comparisons with our work, we make the implementa- tion of the algorithms studied in this paper publicly available as part of the Menpo Project.Comment: 39 page

Testing Colour-Appearance Models: Guidelines for Coordinated Research

These guidelines provide an overview of the many issues involved in generating visual data that can be used to evaluate the performance of colour-appearance models. the three main sections of these guidelines outline the parameters that must be evaluated and controlled in experimental setups for colour-appearance experiments, suggested psychophysical techniques for gathering the data, and some suggested techniques for data analysis. Experimental parameters addressed include models to be tested, illumination conditions, background and surround conditions, types of stimuli to be used, and issues relating to viewing technique. the psychophysical techniques of magnitude estimation, matching, and direct model testing (paired comparison) are described. Data analysis techniques for the evaluation of colour-appearance scales, corresponding-colours data, and model performance scales are suggested

Transpiration and moisture evolution in packaged fresh horticultural produce and the role of integrated mathematical models: A review

Transpiration has various adverse effects on postharvest quality and the shelf-life of fresh fruit and vegetables (FFV). If not controlled, the water released through this process results in direct mass loss and moisture condensation inside packaged FFV. Condensation represents a threat to the product quality as water may accumulate on the product surface and/or packaging system, causing defects in external appearance and promoting growth of spoilage microorganisms. Thus, moisture regulation is extremely important for extending FFV shelf-life. This review focuses on transpiration phenomenon and moisture evolution in packaged fresh horticultural produce. It provides recent information on various moisture control strategies suitable for packaging of fresh horticultural produce. It also provides an evaluation on the role and application of integrative mathematical modelling in describing water relations of FFV for packaging design, as well as, an overview of models reported in literature

Asymptotic and effective coarsening exponents in surface growth models

We consider a class of unstable surface growth models, z_t = -\partial_x J, developing a mound structure of size lambda and displaying a perpetual coarsening process, i.e. an endless increase in time of lambda. The coarsening exponents n, defined by the growth law of the mound size lambda with time, lambda=t^n, were previously found by numerical integration of the growth equations [A. Torcini and P. Politi, Eur. Phys. J. B 25, 519 (2002)]. Recent analytical work now allows to interpret such findings as finite time effective exponents. The asymptotic exponents are shown to appear at so large time that cannot be reached by direct integration of the growth equations. The reason for the appearance of effective exponents is clearly identified.Comment: 6 pages. Several parts and conclusions have been rewritten. (Addendum to the article that can be found in http://www.arxiv.org/abs/cond-mat/0110058