# Indoor dense depth map at drone hovering

## TL;DR

This paper presents a novel method for reconstructing dense depth maps in indoor environments for drone navigation, utilizing sparse point clouds from vSLAM and local plane fitting techniques during drone hovering.

## Contribution

It introduces a patch-based local plane fitting approach combined with a plane sweep technique, improving dense depth estimation in low-textured indoor scenes during small drone motions.

## Key findings

- Better depth reconstruction in artificial lighting conditions
- Improved performance in low-textured environments
- Effective during small camera motions

## 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 artificial lighting condition, low-textured environment compared to earlier literature in small motion.