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- W2890132025 abstract "Autonomous Micro Aerial Vehicles (MAVs) gained tremendous attention in recent years. Autonomous flight in indoor requires a dense depth map for navigable space detection which is the fundamental component for autonomous navigation. In this paper, we address the problem of reconstructing dense depth while a drone is hovering (small camera motion) in indoor scenes using already estimated cameras and sparse point cloud obtained from a vSLAM. We start by segmenting the scene based on sudden depth variation using sparse 3D points and introduce a patch-based local plane fitting via energy minimization which combines photometric consistency and co-planarity with neighbouring patches. The method also combines a plane sweep technique for image segments having almost no sparse point for initialization. Experiments show, the proposed method produces better depth for indoor in arti-ficial lighting condition, low-textured environment compared to earlier literature in small motion." @default.
- W2890132025 created "2018-09-27" @default.
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- W2890132025 date "2018-10-01" @default.
- W2890132025 modified "2023-09-25" @default.
- W2890132025 title "Indoor Dense Depth Map at Drone Hovering" @default.
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- W2890132025 doi "https://doi.org/10.1109/icip.2018.8451442" @default.
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