59 research outputs found

    Target Detection, Indoor Scene Classification, Visual and three-dimensional mapping for service robots in Healthcare

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    This thesis details work on different techniques used to implement service robots for indoor environments. This included two machine learning techniques: Target Detection and Indoor scene classification as well as two computer vision techniques: Visual mapping and three-dimensional mapping. Using these techniques, we tried to make service robots better in environments like hospitals. Assistance provided by service robots will help staff in managing tedious tasks without any problem. We used different techniques for mapping and localization so service robots can autonomously navigate from floor to floor. Depth cameras were used to make recognition and mapping better for indoor environments

    The robot's vista space : a computational 3D scene analysis

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    Swadzba A. The robot's vista space : a computational 3D scene analysis. Bielefeld (Germany): Bielefeld University; 2011.The space that can be explored quickly from a fixed view point without locomotion is known as the vista space. In indoor environments single rooms and room parts follow this definition. The vista space plays an important role in situations with agent-agent interaction as it is the directly surrounding environment in which the interaction takes place. A collaborative interaction of the partners in and with the environment requires that both partners know where they are, what spatial structures they are talking about, and what scene elements they are going to manipulate. This thesis focuses on the analysis of a robot's vista space. Mechanisms for extracting relevant spatial information are developed which enable the robot to recognize in which place it is, to detect the scene elements the human partner is talking about, and to segment scene structures the human is changing. These abilities are addressed by the proposed holistic, aligned, and articulated modeling approach. For a smooth human-robot interaction, the computed models should be aligned to the partner's representations. Therefore, the design of the computational models is based on the combination of psychological results from studies on human scene perception with basic physical properties of the perceived scene and the perception itself. The holistic modeling realizes a categorization of room percepts based on the observed 3D spatial layout. Room layouts have room type specific features and fMRI studies have shown that some of the human brain areas being active in scene recognition are sensitive to the 3D geometry of a room. With the aligned modeling, the robot is able to extract the hierarchical scene representation underlying a scene description given by a human tutor. Furthermore, it is able to ground the inferred scene elements in its own visual perception of the scene. This modeling follows the assumption that cognition and language schematize the world in the same way. This is visible in the fact that a scene depiction mainly consists of relations between an object and its supporting structure or between objects located on the same supporting structure. Last, the articulated modeling equips the robot with a methodology for articulated scene part extraction and fast background learning under short and disturbed observation conditions typical for human-robot interaction scenarios. Articulated scene parts are detected model-less by observing scene changes caused by their manipulation. Change detection and background learning are closely coupled because change is defined phenomenologically as variation of structure. This means that change detection involves a comparison of currently visible structures with a representation in memory. In range sensing this comparison can be nicely implement as subtraction of these two representations. The three modeling approaches enable the robot to enrich its visual perceptions of the surrounding environment, the vista space, with semantic information about meaningful spatial structures useful for further interaction with the environment and the human partner

    The attentive robot companion: learning spatial information from observation and verbal interaction

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    Ziegler L. The attentive robot companion: learning spatial information from observation and verbal interaction. Bielefeld: Universität Bielefeld; 2015.This doctoral thesis investigates how a robot companion can gain a certain degree of situational awareness through observation and interaction with its surroundings. The focus lies on the representation of the spatial knowledge gathered constantly over time in an indoor environment. However, from the background of research on an interactive service robot, methods for deployment in inference and verbal communication tasks are presented. The design and application of the models are guided by the requirements of referential communication. The approach here involves the analysis of the dynamic properties of structures in the robot’s field of view allowing it to distinguish objects of interest from other agents and background structures. The use of multiple persistent models representing these dynamic properties enables the robot to track changes in multiple scenes over time to establish spatial and temporal references. This work includes building a coherent representation considering allocentric and egocentric aspects of spatial knowledge for these models. Spatial analysis is extended with a semantic interpretation of objects and regions. This top-down approach for generating additional context information enhances the grounding process in communication. A holistic, boosting-based classification approach using a wide range of 2D and 3D visual features anchored in the spatial representation allows the system to identify room types. The process of grounding referential descriptions from a human interlocutor in the spatial representation is evaluated through referencing furniture. This method uses a probabilistic network for handling ambiguities in the descriptions and employs a strategy for resolving conflicts. In order to approve the real-world applicability of these approaches, this system was deployed on the mobile robot BIRON in a realistic apartment scenario involving observation and verbal interaction with an interlocutor

    Human experience in the natural and built environment : implications for research policy and practice

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    22nd IAPS conference. Edited book of abstracts. 427 pp. University of Strathclyde, Sheffield and West of Scotland Publication. ISBN: 978-0-94-764988-3

    Human pose estimation from video and inertial sensors

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    [no abstract

    3D compositional hierarchies for object categorization

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    Deep learning methods have become the default tool for image classification. However, application of deep learning to surface shape classification is burdened by the limitations of existing methods, in particular, by lack of invariance to geometric transformations of input data. This thesis proposes two novel frameworks for learning a multi-layer representation of surface shape features, namely the view-based and the surface-based compositional hierarchical frameworks. The proposed representation is a hierarchical vocabulary of shape features, termed parts. Parts of the first layer are pre-defined, while parts of the subsequent layers, describing spatial relations of subparts, are learned. The view-based framework describes spatial relations between subparts using a camera-based reference frame. The key stage of the learning algorithm is part selection which forms the vocabulary based on multi-objective optimization, considering different importance measures of parts. Our experiments show that this framework enables efficient category recognition on a large-scale dataset. The surface-based framework exploits part-based intrinsic reference frames, which are computed for lower layers parts and inherited by parts of the subsequent layers. During learning spatial relations between subparts are described in these reference frames. During inference, a part is detected in input data when its subparts are detected at certain positions and orientations in each other’s reference frames. Since rigid body transformations don’t change positions and orientations of parts in intrinsic reference frames, this approach enables efficient recognition from unseen poses. Experiments show that this framework exhibits a large discriminative power and greater robustness to rigid body transformations than advanced CNN-based methods
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