Coverage-Recon: Coordinated Multi-Drone Image Sampling with Online Map Feedback
Muhammad Hanif, Reiji Terunuma, Takumi Sumino, Kelvin Cheng, and Takeshi Hatanaka

TL;DR
Coverage-Recon introduces a coordinated multi-drone image sampling method that uses real-time map feedback and a QP-based controller to enhance 3D reconstruction quality during flight.
Contribution
It presents a novel online feedback-driven coverage control algorithm for multi-drone systems that improves 3D map reconstruction accuracy and completeness.
Findings
Online feedback improves reconstruction quality.
Coverage-Recon outperforms conventional methods.
Real-time mesh updates guide drone motion effectively.
Abstract
This article addresses collaborative 3D map reconstruction using multiple drones. Achieving high-quality reconstruction requires capturing images of keypoints within the target scene from diverse viewing angles, and coverage control offers an effective framework to meet this requirement. Meanwhile, recent advances in real-time 3D reconstruction algorithms make it possible to render an evolving map during flight, enabling immediate feedback to guide drone motion. Building on this, we present Coverage-Recon, a novel coordinated image sampling algorithm that integrates online map feedback to improve reconstruction quality on-the-fly. In Coverage-Recon, the coordinated motion of drones is governed by a Quadratic Programming (QP)-based angle-aware coverage controller, which ensures multi-viewpoint image capture while enforcing safety constraints. The captured images are processed in real…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Vision and Imaging · 3D Surveying and Cultural Heritage
