ClusterFusion: Real-time Relative Positioning and Dense Reconstruction for UAV Cluster

TL;DR

This paper introduces ClusterFusion, a framework enabling real-time dense point cloud reconstruction and accurate relative positioning in UAV clusters, overcoming limitations of single UAV systems in large-scale mapping tasks.

Contribution

The paper presents a novel cluster framework with improved visual odometry and joint optimization algorithms for real-time dense reconstruction and precise UAV relative positioning.

Findings

Effective suppression of scale drift in large-scale mapping

Real-time dense point cloud generation with increased speed using more UAVs

Demonstrated robustness and accuracy on real-world data

Abstract

As robotics technology advances, dense point cloud maps are increasingly in demand. However, dense reconstruction using a single unmanned aerial vehicle (UAV) suffers from limitations in flight speed and battery power, resulting in slow reconstruction and low coverage. Cluster UAV systems offer greater flexibility and wider coverage for map building. Existing methods of cluster UAVs face challenges with accurate relative positioning, scale drift, and high-speed dense point cloud map generation. To address these issues, we propose a cluster framework for large-scale dense reconstruction and real-time collaborative localization. The front-end of the framework is an improved visual odometry which can effectively handle large-scale scenes. Collaborative localization between UAVs is enabled through a two-stage joint optimization algorithm and a relative pose optimization algorithm, effectively achieving accurate relative positioning of UAVs and mitigating scale drift. Estimated poses are used to achieve real-time dense reconstruction and fusion of point cloud maps. To evaluate the performance of our proposed method, we conduct qualitative and quantitative experiments on real-world data. The results demonstrate that our framework can effectively suppress scale drift and generate large-scale dense point cloud maps in real-time, with the reconstruction speed increasing as more UAVs are added to the system.