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Stały URI dla kolekcjihttp://hdl.handle.net/11652/4775

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20231

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Autorsearch.filters.applied.operator.equals: search.filters.author.Kostusiak, AleksanderTematsearch.filters.applied.operator.equals: search.filters.subject.deep learning

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  • Pozycja
    Improving RGB-D Visual Odometry with Depth Learned from a Better Sensor’s Output
    (Wydawnictwo Politechniki Łódzkiej, 2023) Kostusiak, Aleksander
    This paper compares the results obtained from an indoor Visual Odometry (VO) system with RGB-D images provided by a Kinect v1 camera against those achieved by a VO with enhanced depth channel. For this purpose, we have used two classic image inpainting methods and a deeplearning approach for scene depth estimation employing Kinect v2 depth maps as reference data. The ability to enhance lower-quality data is crucial to reduce the cost of VO applications because higher-quality information can be infused through deep learning in systems using budget sensors.