Indoor dense depth map at drone hovering
This work addresses the challenge of enabling autonomous flight for drones in indoor settings, which is an incremental improvement for specific navigation tasks.
The paper tackles the problem of reconstructing dense depth maps for indoor drone navigation under small camera motion, using sparse point clouds from vSLAM and a patch-based local plane fitting method with energy minimization. The result shows improved depth estimation in artificial lighting and low-textured environments compared to prior methods.
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 artificial lighting condition, low-textured environment compared to earlier literature in small motion.