7,480 research outputs found

    Development of a Sun centred sky camera for use in UV measurements

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    Event-based Vision: A Survey

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    Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

    Cue combination for 3D location judgements

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    Cue combination rules have often been applied to the perception of surface shape but not to judgements of object location. Here, we used immersive virtual reality to explore the relationship between different cues to distance. Participants viewed a virtual scene and judged the change in distance of an object presented in two intervals, where the scene changed in size between intervals (by a factor of between 0.25 and 4). We measured thresholds for detecting a change in object distance when there were only 'physical' (stereo and motion parallax) or 'texture-based' cues (independent of the scale of the scene) and used these to predict biases in a distance matching task. Under a range of conditions, in which the viewing distance and position of the tarte relative to other objects was varied, the ration of 'physical' to 'texture-based' thresholds was a good predictor of biases in the distance matching task. The cue combination approach, which successfully accounts for our data, relies on quite different principles from those underlying geometric reconstruction

    People tracking by cooperative fusion of RADAR and camera sensors

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    Accurate 3D tracking of objects from monocular camera poses challenges due to the loss of depth during projection. Although ranging by RADAR has proven effective in highway environments, people tracking remains beyond the capability of single sensor systems. In this paper, we propose a cooperative RADAR-camera fusion method for people tracking on the ground plane. Using average person height, joint detection likelihood is calculated by back-projecting detections from the camera onto the RADAR Range-Azimuth data. Peaks in the joint likelihood, representing candidate targets, are fed into a Particle Filter tracker. Depending on the association outcome, particles are updated using the associated detections (Tracking by Detection), or by sampling the raw likelihood itself (Tracking Before Detection). Utilizing the raw likelihood data has the advantage that lost targets are continuously tracked even if the camera or RADAR signal is below the detection threshold. We show that in single target, uncluttered environments, the proposed method entirely outperforms camera-only tracking. Experiments in a real-world urban environment also confirm that the cooperative fusion tracker produces significantly better estimates, even in difficult and ambiguous situations

    Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

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    MOMA: Visual Mobile Marker Odometry

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    In this paper, we present a cooperative odometry scheme based on the detection of mobile markers in line with the idea of cooperative positioning for multiple robots [1]. To this end, we introduce a simple optimization scheme that realizes visual mobile marker odometry via accurate fixed marker-based camera positioning and analyse the characteristics of errors inherent to the method compared to classical fixed marker-based navigation and visual odometry. In addition, we provide a specific UAV-UGV configuration that allows for continuous movements of the UAV without doing stops and a minimal caterpillar-like configuration that works with one UGV alone. Finally, we present a real-world implementation and evaluation for the proposed UAV-UGV configuration

    Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

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    One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field Robotic
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