Fusion-SLAM by combining RGB-D SLAM and Rat SLAM : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics at Massey University, Albany, New Zealand

Robotic Simultaneous Localization and Mapping (SLAM) is the problem of solving how to create a map of the environment while localizing the robot in the map being created. This presents a causality dilemma where the map needs to be created in order to localize the robot, but the robot also needs to be localized in order to create the map. In past research there have been many solutions to this problem ranging from Extended Kalman Filter (EKF) to Graph SLAM systems. There has also been extensive research in bioinspired methods, like ratSLAM implemented in aerial and land-based robots. The different research setups use sensors such as Time of Flight (ToF) e.g. laser scanners and passive devices e.g. cameras. Over the past few years a new type of combined apparatus has been developed by Microsoft called the Kinect. It combines active and passive sensing elements and aligns the data in a way which allows for efficient implementation in robotic systems. This has led to the Kinect being implemented in new research and many studies, mostly around RGB-D SLAM. However these methods generally require a continuous stream of images and become inaccurate when exposed to ambiguous environments. This thesis presents the design and implementation of a fusion algorithm to solve the robotic SLAM problem. The study starts by analysing existing methods to determine what research has been done. It then proceeds to introduce the components used in this study and the Fusion Algorithm. The algorithm incorporates the colour and depth data extraction and manipulation methods used in the RGB-D SLAM system while also implementing a mapping step similar to the grid cell and firing field functions found in the ratSLAM. This method improves upon the RGB-D SLAM’s weakness of requiring a continuous stream and ambiguous images. An experiment is then conducted on the developed system to determine the extent to which it has solved the SLAM problem. Moreover, the success rate for finding a node in a cell and matching its pose is also investigated. In conclusion, this research presents a novel algorithm for successfully solving the robotic SLAM problem. The proposed algorithm also helps improve the system’s efficiency in navigation, odometry error correction, and scan matching vulnerabilities in feature sparse views

Oscillating terms in the Renyi entropy of Fermi liquids

In this work we compute subleading oscillating terms in the Renyi entropy of Fermi gases and Fermi liquids corresponding to $2k_F$-like oscillations. Our theoretical tools are the one dimensional formulation of Fermi liquid entanglement familiar from discussions of the logarithmic violation of the area law and quantum Monte Carlo calculations. The main result is a formula for the oscillating term for any region geometry and a spherical Fermi surface. We compare this term to numerical calculations of entanglement using the correlation function method and find excellent agreement. We also compare with quantum Monte Carlo data on interacting Fermi liquids where we also find excellent agreement up to moderate interaction strengths.Comment: 8 pages, 2 figure

The Bracing Requirements of Steel Beams of Intermediate Slenderness

Steel beams, whether rolled or built-up, contain unavoidable initial imperfections and residual stresses and are subject to unintentional eccentricity of applied loading. Such beams which also possess inadequate lateral restraint are prone to failure as a result of lateral-torsional instability, which occurs under elastic or inelastic conditions depending on the slenderness of the member. A review of the literature pertaining to the bracing requirements of steel beams revealed little published work concerned with the restraint of beams of intermediate and low slenderness which fail inelastically. The provision of adequate midspan restraint for the prevention of inelastic instability in centrally loaded, single span I-beams formed the basis of this study. The non-linear analysis capabilities of the finite element programmes MSC/NASTRAN and FINAS were employed to provide theoretical verification of the results of a series of tests on small-scale, fabricated, steel I-beams. Measured initial geometrical imperfections of the test beams were modelled in the finite element idealisation by suitable adjustment of nodal coordinates and both geometrical and material non-linearites were accounted for in the analysis. Numerical instability and convergence difficulties were encountered in both analyses, although their occurrence was less frequent in FINAS. In FINAS analyses where these difficulties did not arise, collapse loads were determined and post-buckling behaviour followed with relative ease. A bracing fork device for the provision of a predetermined stiffness of midspan restraint was developed and subsequently employed in all tests. Strain gauges attached to the prongs of this device permitted bracing forces to be measured at any stage in the tests. In general, satisfactory correlation was achieved between finite element and experimental results, allowing bracing criteria for single span, centrally loaded and restrained beams to be proposed. As anticipated, the bracing requirements of inelastic beams proved more onerous than those demanded by the classical bifurcation analysis employed in problems of elastic beam buckling. A subsequent series of comparative designs in accordance with the three current (1985) British steelwork codes (BS 449, BS 5950 and BS 5400) revealed that bracing members designed as struts in compliance with the minimum strength and maximum slenderness criteria of these documents provided adequate stiffness and strength of restraint

A Translational Zebrafish Model to Study Breast Cancer Inflammation and Metastasis

Though modern medicine has greatly improved detection and treatment of breast cancer and overall survival rates are around 85%, this disease is still the second most fatal cancer in Canada. Once the cancer becomes metastatic, it is considered incurable and treatment strategies are directed towards maintenance of the disease rather than curing it. Another hurdle to breast cancer treatment is the severity of side effects from chemotherapeutics, for example cardiotoxicity and hepatotoxicity, that are sometimes fatal. Dexamethasone (Dex) is a synthetic glucocorticoid (GC) that has been shown to be effective at reducing the less severe side effects of chemotherapeutics, such as nausea and inflammation. There is growing concern that Dex interferes with the effectiveness of anti-cancer drugs because of chronic suppression of the immune system, which has been implicated in cancer progression in some inflammatory diseases. The use of natural health products (NHPs) to treat inflammation is a growing field of research to find alternatives to synthetic GCs and many are already on the market. To study the toxicity of drug combinations there needs to be an efficient model that accurately incorporates immune response and organ toxicity. Zebrafish have become an increasingly used animal model to study human cancer and drug toxicity because they are cost effective and can be used for high throughput assays. Using a zebrafish model optimized for this work, we show that Dex increases the metastatic potential of breast cancer cells and accentuates the cardiotoxicity and hepatotoxicity of embryos when treated in combination with cyclophosphamide but not with paclitaxel. We also show that the NHP, Nutria plus, has anti-inflammatory and antioxidant properties and may be a beneficial supplement for treating inflammatory diseases and preventing cancer drug toxicity. Together these results show that the ubiquitous use of Dex in clinics should be re-evaluated. We also studied cell cycle regulation of mammary acini development and cancer metastasis. We show, for the first time, that increased expression of the cell cycle regulator, Spy1, leads to multi-acinar mammary alveolar structures in vitro, and leads to increased metastasis of breast cells in an in vivo zebrafish model, introducing Spy1 as a potential target for treatment of metastatic breast cancer

SPRING DISCHARGE MONITORING IN LOW-RESOURCE SETTINGS: A CASE STUDY OF CONCEPCIÓN CHIQUIRICHAPA, GUATEMALA

Water springs are the principal source of water for many localities in Central America, including the municipality of Concepción Chiquirichapa in the Western Highlands of Guatemala. Long-term monitoring records are critical for informed water management as well as resource forecasting, though data are scarce and monitoring in low-resource settings presents special challenges. Spring discharge was monitored monthly in six municipal springs during the author’s Peace Corps assignment, from May 2011 to March 2012, and water level height was monitored in two spring boxes over the same time period using automated water-level loggers. The intention of this approach was to circumvent the need for frequent and time-intensive manual measurement by identifying a fixed relationship between discharge and water level. No such relationship was identified, but the water level record reveals that spring yield increased for four months following Tropical Depression 12E in October 2011. This suggests that the relationship between extreme precipitation events and long-term water spring yields in Concepción should be examined further. These limited discharge data also indicate that aquifer baseflow recession and catchment water balance could be successfully characterized if a long-term discharge record were established. This study also presents technical and social considerations for selecting a methodology for spring discharge measurement and highlights the importance of local interest in conducting successful community-based research in intercultural low-resource settings

Dynamical Mean-Field Theory Simulations with the Adaptive Sampling Configuration Interaction Method

In the pursuit of accurate descriptions of strongly correlated quantum many-body systems, dynamical mean-field theory (DMFT) has been an invaluable tool for elucidating the spectral properties and quantum phases of both phenomenological models and ab initio descriptions of real materials. Key to the DMFT process is the self-consistent map of the original system into an Anderson impurity model, the ground state of which is computed using an impurity solver. The power of the method is thus limited by the complexity of the impurity model the solver can handle. Simulating realistic systems generally requires many correlated sites. By adapting the recently proposed adaptive sampling configuration interaction (ASCI) method as an impurity solver, we enable much more efficient zero temperature DMFT simulations. The key feature of the ASCI method is that it selects only the most relevant Hilbert space degrees of freedom to describe the ground state. This reduces the numerical complexity of the calculation, which will allow us to pursue future DMFT simulations with more correlated impurity sites than in previous works. Here we present the ASCI-DMFT method and example calculations on the one-dimensional and two-dimensional Hubbard models that exemplify its efficient convergence and timing properties. We show that the ASCI approach is several orders of magnitude faster than the current best published ground state DMFT simulations, which allows us to study the bath discretization error in simulations with small clusters, as well as to address cluster sizes beyond the current state of the art. Our approach can also be adapted for other embedding methods such as density matrix embedding theory and self-energy embedding theory.Comment: 12 pages, 11 figures, supplemental informatio