2,885 research outputs found

    GASP : Geometric Association with Surface Patches

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    A fundamental challenge to sensory processing tasks in perception and robotics is the problem of obtaining data associations across views. We present a robust solution for ascertaining potentially dense surface patch (superpixel) associations, requiring just range information. Our approach involves decomposition of a view into regularized surface patches. We represent them as sequences expressing geometry invariantly over their superpixel neighborhoods, as uniquely consistent partial orderings. We match these representations through an optimal sequence comparison metric based on the Damerau-Levenshtein distance - enabling robust association with quadratic complexity (in contrast to hitherto employed joint matching formulations which are NP-complete). The approach is able to perform under wide baselines, heavy rotations, partial overlaps, significant occlusions and sensor noise. The technique does not require any priors -- motion or otherwise, and does not make restrictive assumptions on scene structure and sensor movement. It does not require appearance -- is hence more widely applicable than appearance reliant methods, and invulnerable to related ambiguities such as textureless or aliased content. We present promising qualitative and quantitative results under diverse settings, along with comparatives with popular approaches based on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201

    Efficient 2D-3D Matching for Multi-Camera Visual Localization

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    Visual localization, i.e., determining the position and orientation of a vehicle with respect to a map, is a key problem in autonomous driving. We present a multicamera visual inertial localization algorithm for large scale environments. To efficiently and effectively match features against a pre-built global 3D map, we propose a prioritized feature matching scheme for multi-camera systems. In contrast to existing works, designed for monocular cameras, we (1) tailor the prioritization function to the multi-camera setup and (2) run feature matching and pose estimation in parallel. This significantly accelerates the matching and pose estimation stages and allows us to dynamically adapt the matching efforts based on the surrounding environment. In addition, we show how pose priors can be integrated into the localization system to increase efficiency and robustness. Finally, we extend our algorithm by fusing the absolute pose estimates with motion estimates from a multi-camera visual inertial odometry pipeline (VIO). This results in a system that provides reliable and drift-less pose estimation. Extensive experiments show that our localization runs fast and robust under varying conditions, and that our extended algorithm enables reliable real-time pose estimation.Comment: 7 pages, 5 figure

    Comparison of in-sight and handheld navigation devices toward supporting industry 4.0 supply chains: First and last mile deliveries at the human level

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    Last (and First) mile deliveries are an increasingly important and costly component of supply chains especially those that require transport within city centres. With reduction in anticipated manufacturing and delivery timescales, logistics personnel are expected to identify the correct location (accurately) and supply the goods in appropriate condition (safe delivery). Moving towards more environmentally sustainable supply chains, the last/first mile of deliveries may be completed by a cyclist courier which could result in significant reductions in congestion and emissions in cities. In addition, the last metres of an increasing number of deliveries are completed on foot i.e. as a pedestrian. Although research into new technologies to support enhanced navigation capabilities is ongoing, the focus to date has been on technical implementations with limited studies addressing how information is perceived and actioned by a human courier. In the research reported in this paper a comparison study has been conducted with 24 participants evaluating two examples of state-of-the-art navigation aids to support accurate (right time and place) and safe (right condition) navigation. Participants completed 4 navigation tasks, 2 whilst cycling and 2 whilst walking. The navigation devices under investigation were a handheld display presenting a map and instructions and an in-sight monocular display presenting text and arrow instructions. Navigation was conducted in a real-world environment in which eye movements and device interaction were recorded using Tobii-Pro 2 eye tracking glasses. The results indicate that the handheld device provided better support for accurate navigation (right time and place), with longer but less frequent gaze interactions and higher perceived usability. The in-sight display supported improved situation awareness with a greater number of hazards acknowledged. The benefits and drawbacks of each device and use of visual navigation support tools are discussed

    Vision-based methods for state estimation and control of robotic systems with application to mobile and surgical robots

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    For autonomous systems that need to perceive the surrounding environment for the accomplishment of a given task, vision is a highly informative exteroceptive sensory source. When gathering information from the available sensors, in fact, the richness of visual data allows to provide a complete description of the environment, collecting geometrical and semantic information (e.g., object pose, distances, shapes, colors, lights). The huge amount of collected data allows to consider both methods exploiting the totality of the data (dense approaches), or a reduced set obtained from feature extraction procedures (sparse approaches). This manuscript presents dense and sparse vision-based methods for control and sensing of robotic systems. First, a safe navigation scheme for mobile robots, moving in unknown environments populated by obstacles, is presented. For this task, dense visual information is used to perceive the environment (i.e., detect ground plane and obstacles) and, in combination with other sensory sources, provide an estimation of the robot motion with a linear observer. On the other hand, sparse visual data are extrapolated in terms of geometric primitives, in order to implement a visual servoing control scheme satisfying proper navigation behaviours. This controller relies on visual estimated information and is designed in order to guarantee safety during navigation. In addition, redundant structures are taken into account to re-arrange the internal configuration of the robot and reduce its encumbrance when the workspace is highly cluttered. Vision-based estimation methods are relevant also in other contexts. In the field of surgical robotics, having reliable data about unmeasurable quantities is of great importance and critical at the same time. In this manuscript, we present a Kalman-based observer to estimate the 3D pose of a suturing needle held by a surgical manipulator for robot-assisted suturing. The method exploits images acquired by the endoscope of the robot platform to extrapolate relevant geometrical information and get projected measurements of the tool pose. This method has also been validated with a novel simulator designed for the da Vinci robotic platform, with the purpose to ease interfacing and employment in ideal conditions for testing and validation. The Kalman-based observers mentioned above are classical passive estimators, whose system inputs used to produce the proper estimation are theoretically arbitrary. This does not provide any possibility to actively adapt input trajectories in order to optimize specific requirements on the performance of the estimation. For this purpose, active estimation paradigm is introduced and some related strategies are presented. More specifically, a novel active sensing algorithm employing visual dense information is described for a typical Structure-from-Motion (SfM) problem. The algorithm generates an optimal estimation of a scene observed by a moving camera, while minimizing the maximum uncertainty of the estimation. This approach can be applied to any robotic platforms and has been validated with a manipulator arm equipped with a monocular camera

    Erg-O: ergonomic optimization of immersive virtual environments

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    Interaction in VR involves large body movements, easily inducing fatigue and discomfort. We propose Erg-O, a manipulation technique that leverages visual dominance to maintain the visual location of the elements in VR, while making them accessible from more comfortable locations. Our solution works in an open-ended fashion (no prior knowledge of the object the user wants to touch), can be used with multiple objects, and still allows interaction with any other point within user's reach. We use optimization approaches to compute the best physical location to interact with each visual element, and space partitioning techniques to distort the visual and physical spaces based on those mappings and allow multi-object retargeting. In this paper we describe the Erg-O technique, propose two retargeting strategies and report the results from a user study on 3D selection under different conditions, elaborating on their potential and application to specific usage scenarios

    Erg-O: Ergonomic Optimization of Immersive Virtual Environments

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    Interaction in VR involves large body movements, easily inducing fatigue and discomfort. We propose Erg-O, a manipulation technique that leverages visual dominance to maintain the visual location of the elements in VR, while making them accessible from more comfortable locations. Our solution works in an open-ended fashion (no prior knowledge of the object the user wants to touch), can be used with multiple objects, and still allows interaction with any other point within user's reach. We use optimization approaches to compute the best physical location to interact with each visual element, and space partitioning techniques to distort the visual and physical spaces based on those mappings and allow multi-object retargeting. In this paper we describe the Erg-O technique, propose two retargeting strategies and report the results from a user study on 3D selection under different conditions, elaborating on their potential and application to specific usage scenarios

    Computational interaction techniques for 3D selection, manipulation and navigation in immersive VR

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    3D interaction provides a natural interplay for HCI. Many techniques involving diverse sets of hardware and software components have been proposed, which has generated an explosion of Interaction Techniques (ITes), Interactive Tasks (ITas) and input devices, increasing thus the heterogeneity of tools in 3D User Interfaces (3DUIs). Moreover, most of those techniques are based on general formulations that fail in fully exploiting human capabilities for interaction. This is because while 3D interaction enables naturalness, it also produces complexity and limitations when using 3DUIs. In this thesis, we aim to generate approaches that better exploit the high potential human capabilities for interaction by combining human factors, mathematical formalizations and computational methods. Our approach is focussed on the exploration of the close coupling between specific ITes and ITas while addressing common issues of 3D interactions. We specifically focused on the stages of interaction within Basic Interaction Tasks (BITas) i.e., data input, manipulation, navigation and selection. Common limitations of these tasks are: (1) the complexity of mapping generation for input devices, (2) fatigue in mid-air object manipulation, (3) space constraints in VR navigation; and (4) low accuracy in 3D mid-air selection. Along with two chapters of introduction and background, this thesis presents five main works. Chapter 3 focusses on the design of mid-air gesture mappings based on human tacit knowledge. Chapter 4 presents a solution to address user fatigue in mid-air object manipulation. Chapter 5 is focused on addressing space limitations in VR navigation. Chapter 6 describes an analysis and a correction method to address Drift effects involved in scale-adaptive VR navigation; and Chapter 7 presents a hybrid technique 3D/2D that allows for precise selection of virtual objects in highly dense environments (e.g., point clouds). Finally, we conclude discussing how the contributions obtained from this exploration, provide techniques and guidelines to design more natural 3DUIs

    Scene creation and exploration in outdoor augmented reality

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    This thesis investigates Outdoor Augmented Reality (AR) especially for scene creation and exploration aspects.We decompose a scene into several components: a) Device, b) Target Object(s), c) Task, and discuss their interrelations. Based on those relations we outline use-cases and workflows. The main contribution of this thesis is providing AR oriented workflows for selected professional fields specifically for scene creation and exploration purposes, through case studies as well as analyzing the relations between AR scene components. Our contributions inlude, but not limited to: i) analysis of scene components and factoring inherintly available errors, to create a transitional hybrid tracking scheme for multiple targets, ii) a novel image-based approach that uses building block analogy for modelling and introduces volumetric and temporal labeling for annotations, iii) an evaluation of the state of the art X-Ray visualization methods as well as our proposed multi-view method. AR technology and capabilities tend to change rapidly, however we believe the relation between scene components and the practical advantages their analysis provide are valuable. Moreover, we have chosen case studies as diverse as possible in order to cover a wide range of professional field studies. We believe our research is extendible to a variety of field studies for disciplines including but not limited to: Archaeology, architecture, cultural heritage, tourism, stratigraphy, civil engineering, and urban maintenance
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