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Autonomous Micro Aerial Vehicles (MAVs) gained tremendous attention in recentyears. Autonomous flight in indoor requires a dense depth map for navigablespace detection which is the fundamental component for autonomous navigation.In this paper, we address the problem of reconstructing dense depth while adrone is hovering (small camera motion) in indoor scenes using alreadyestimated cameras and sparse point cloud obtained from a vSLAM. We start bysegmenting the scene based on sudden depth variation using sparse 3D points andintroduce a patch-based local plane fitting via energy minimization whichcombines photometric consistency and co-planarity with neighbouring patches.The method also combines a plane sweep technique for image segments havingalmost no sparse point for initialization. Experiments show, the proposedmethod produces better depth for indoor in artificial lighting condition,low-textured environment compared to earlier literature in small motion.

Indoor dense depth map at drone hovering