Exploring The Effect of Meta-Structural Information on the Global Consistency of SLAM

Accurate online estimation of the environment structure simultaneously with the robot pose is a key capability for autonomous robotic vehicles. Classical simultaneous localization and mapping (SLAM) algorithms make no assumptions about the configuration of the points in the environment, however, real world scenes have significant structure (ground planes, buildings, walls, ceilings, etc..) that can be exploited. In this paper, we introduce meta-structural information associated with geometric primitives into the estimation problem and analyze their effect on the global structural consistency of the resulting map. Although we only consider the effect of adding planar and orthogonality information for the estimation of 3D points in a Manhattan-like world, this framework can be extended to any type of geometric, kinematic, dynamic or even semantic information. We evaluate our approach on a city-like simulated environment. We highlight the advantages of the proposed solution over SLAM formulation considering no prior knowledge about the configuration of 3D points in the environment.This research was supported by the Australian Research Council through the “Australian Centre of Excellence for Robotic Vision” CE140100016

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Meta Information in Graph-based Simultaneous Localisation And Mapping

Establishing the spatial and temporal relationships between a robot, and its environment serves as a basis for scene understanding. The established approach in the literature to simultaneously build a representation of the environment, and spatially and temporally localise the robot within the environment, is Simultaneous Localisation And Mapping (SLAM). SLAM algorithms in general, and in particular visual SLAM--where the primary sensors used are cameras--have gained a great amount of attention in the robotics and computer vision communities over the last few decades due to their wide range of applications. The advances in sensing technologies and image-based learning techniques provide an opportunity to introduce additional understanding of the environment to improve the performance of SLAM algorithms. In this thesis, I utilise meta information in a SLAM framework to achieve a robust and consistent representation of the environment and challenge some of the most limiting assumptions in the literature. I exploit structural information associated with geometric primitives, making use of the significant amount of structure present in real world scenes where SLAM algorithms are normally deployed. In particular, I exploit planarity of a group of points and introduce higher-level information associated with orthogonality and parallelism of planes to achieve structural consistency of the returned map. Separately, I also challenge the static world assumption that severely limits the deployment of autonomous mobile robotic systems in a wide range of important real world applications involving highly dynamic and unstructured environments by utilising the semantic and dynamic information in the scene. Most existing techniques try to simplify the problem by ignoring dynamics, relying on a pre-collected database of objects 3D models, imposing some motion constraints or fail to estimate the full SE(3) motions of objects in the scene which makes it infeasible to deploy these algorithms in real life scenarios of unknown and highly dynamic environments. Exploiting semantic and dynamic information in the environment allows to introduce a model-free object-aware SLAM system that is able to achieve robust moving object tracking, accurate estimation of dynamic objects full SE(3) motion, and extract velocity information of moving objects in the scene, resulting in accurate robot localisation and spatio-temporal map estimation

Understanding ethnic minority differences in access to and outcomes of psychological therapies for first episode psychosis and severe mental illness

Context: Prevalence rates of severe mental illnesses (SMI) such as psychosis differ between ethnic groups disproportionately. Disparities also exist when exploring access and outcomes to psychological therapies based on ethnicity. The literature suggests that individuals from ethnic minority groups with SMI are less likely to be offered a psychology therapy. Methods: The broad aim of the thesis was to explore the effectiveness and accessibility of psychological therapies for ethnic minority groups who experience a SMI. A systematic review explored the effectiveness of psychological therapies for ethnic minority groups who experienced a SMI. Secondly, an empirical paper investigated whether sociodemographic factors, including ethnicity, influenced the offer and uptake of psychological therapies in a sample of service users who experienced first episode psychosis. Results: Our systematic review included nine studies for analysis, with seven reporting significant improvements in SMI symptom severity. Seven studies made cultural adaptations which led to a reduction in SMI symptom severity compared to treatment as usual. However the quality and risk of bias varied between studies, reducing the strength of the findings. In our empirical paper we found that service users in ‘White Other’ and ‘Other’ ethnic minority groups were less likely to be offered a psychological therapy compared to the white British reference group (‘White other’ OR = .48, CI .26 – .89, p = .04, ‘Other’ OR = .38, CI .17- .87, p = .02). Presenting to Early Intervention Services increased the likelihood being offered a psychological therapy. Conclusions: Our evidence highlights that whilst psychological therapies may be useful for ethnic minority groups with SMI, the availability is mixed depending on the service accessed. Future research is needed to explore the frequency and use of culturally adapted therapies in clinical settings. Research is needed that allows comparisons to be made between culturally adapted and standard therapies

Data-driven shape analysis and processing

Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing