Global ETD Search

1	Optical Flow for Event Detection Camera Almatrafi, Mohammed Mutlaq January 2019 (has links) No description available. Electrical Engineering Engineering Optical Flow Dynamic Vision Sensor DVS Event Camera Motion Estimation
2	High-speed Imaging with Less Data Baldwin, Raymond Wesley 09 August 2021 (has links) No description available. Computer Engineering Scientific Imaging event camera high-speed Fourier camera spatial light modulation denoising
3	Event-Based Visual SLAM : An Explorative Approach Rideg, Johan January 2023 (has links) Simultaneous Localization And Mapping (SLAM) is an important topic within the field of roboticsaiming to localize an agent in a unknown or partially known environment while simultaneouslymapping the environment. The ability to perform robust SLAM is especially important inhazardous environments such as natural disasters, firefighting and space exploration wherehuman exploration may be too dangerous or impractical. In recent years, neuromorphiccameras have been made commercially available. This new type of sensor does not outputconventional frames but instead an asynchronous signal of events at a microsecond resolutionand is capable of capturing details in complex lightning scenarios where a standard camerawould be either under- or overexposed, making neuromorphic cameras a promising solution insituations where standard cameras struggle. This thesis explores a set of different approachesto virtual frames, a frame-based representation of events, in the context of SLAM.UltimateSLAM, a project fusing events, gray scale and IMU data, is investigated using virtualframes of fixed and varying frame rate both with and without motion compensation. Theresulting trajectories are compared to the trajectories produced when using gray scale framesand the number of detected and tracked features are compared. We also use a traditional visualSLAM project, ORB-SLAM, to investigate the Gaussian weighted virtual frames and gray scaleframes reconstructed from the event stream using a recurrent network model. While virtualframes can be used for SLAM, the event camera is not a plug and play sensor and requires agood choice of parameters when constructing virtual frames, relying on pre-existing knowledgeof the scene. event camera neuromorphic SLAM visual odometry
4	Event-based Visual Odometryusing Asynchronous CornerFeature Detection and Tracking : A Master of Science Thesis / Eventbaserad Visuell Odometri med Asynkron Detektion ochSpårning av Hörn : En Masteruppsats i Datorseende och Signalanalys Torberntsson, William January 2024 (has links) This master thesis, conducted at SAAB Dynamics Linköping, studies Visual Odometry (VO), or camera pose estimation, using a monocular event camera. Event cameras are not yet widely used in the industry, and there is significant interest in understanding the methodological differences between performing Visual Odometry (VO) with event cameras compared to traditional frame cameras. Event cameras have the potential to capture information between frames, which may include data that is lost or not captured by frame cameras. This thesis compares two different feature detectors and evaluates their performance against a frame-based method. Visual Odometry was conducted both with and without known 3D points. However, attempts to perform VO without known 3D points did not yield a robust pipeline within the limited time frame of this thesis, and thus was not further developed to function with a purely event-based method. On the other hand, VO performance with known 3D points achieved continues 6-DoF pose estimation. The results demonstrate that event cameras have the potential to detect and track features in challenging scenes, such as those with dark or bright lighting conditions, for example, objects passing by the sun. This thesis suggests that implementing a robust 6-DoF pose estimation would be feasible with a more reliable 3D-2D point pair matching technique or a more sophisticated VO pipeline. computer vision visual odometry event camera Arc* DTFS-eHarris tracking asynchronous event-based corner tracking event-based corner detection DAVIS DVXplorer
5	Asynchronous Event-Feature Detection and Tracking for SLAM Initialization Ta, Tai January 2024 (has links) Traditional cameras are most commonly used in visual SLAM to provide visual information about the scene and positional information about the camera motion. However, in the presence of varying illumination and rapid camera movement, the visual quality captured by traditional cameras diminishes. This limits the applicability of visual SLAM in challenging environments such as search and rescue situations. The emerging event camera has been shown to overcome the limitations of the traditional camera with the event camera's superior temporal resolution and wider dynamic range, opening up new areas of applications and research for event-based SLAM. In this thesis, several asynchronous feature detectors and trackers will be used to initialize SLAM using event camera data. To assess the pose estimation accuracy between the different feature detectors and trackers, the initialization performance was evaluated from datasets captured from various environments. Furthermore, two different methods to align corner-events were evaluated on the datasets to assess the difference. Results show that besides some slight variation in the number of accepted initializations, the alignment methods show no overall difference in any metric. Overall highest performance among the event-based trackers for initialization is HASTE with mostly high pose accuracy and a high number of accepted initializations. However, the performance degrades in featureless scenes. CET on the other hand shows mostly lower performance compared to HASTE. Visual SLAM SLAM Traditional cameras event camera events neuromorphic camera silicon retina dynamic vision sensor Camera motion Illumination variation Rapid camera movement Visual quality degradation Challenging environments Search and rescue Event cameras Temporal resolution Dynamic range Event-based SLAM Asynchronous feature detectors Trackers Pose estimation accuracy Initialization performance Datasets Corner-event alignment HASTE tracker Pose accuracy Featureless scenes CET tracker Initialization acceptance Performance comparison Signal Processing Signalbehandling Robotics Robotteknik och automation Control Engineering Reglerteknik

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