Quality-Adaptive Multi-UAV 3D Reconstruction with Sparse Workload Redistribution

2026-07-27

AI Summary

This work addresses the challenges of online coordination and the trade-off between quality and efficiency in multi-UAV collaborative 3D reconstruction. The authors propose a quality-adaptive, decentralized decision-making framework that leverages TSDF-based confidence to guide viewpoint selection and information gain evaluation. A two-level coordination mechanism is introduced: locally, a penalty factor encourages spatial dispersion among UAVs; globally, regularized clustering combined with optimal task assignment enables sparse rescheduling, triggered only upon detection of workload imbalance. Experimental results demonstrate that the proposed method significantly improves path efficiency, scene coverage, and reconstruction accuracy while meeting user-specified fidelity requirements.

Abstract

3D reconstruction of unknown environments is a key application in robotics but is severely limited by the computational and energy capabilities of current aerial platforms. Deploying multiple UAVs and providing efficient and scalable path planning strategies are common approaches, but effective online coordination among UAVs remains a significant challenge. To address this problem, we propose a quality-adaptive decentralized decision-making strategy to build a 3D map with user-defined degrees of fidelity. The approach integrates a quality-oriented criterion based on TSDF confidence into view generation and information gain estimation to produce viewpoints consistent with the desired fidelity target. Additionally, we employ two levels of coordination: a penalty factor in the viewpoint evaluation to encourage local dispersion among the UAVs and a global imbalance correction mechanism. The latter, based on regularized clustering and optimal task assignment, is only triggered when an unbalanced configuration relative to high-information regions is detected. Simulation results demonstrate that the proposed method improves path efficiency compared to state-of-the-art multi-UAV exploration approaches, while also achieving higher-fidelity reconstructions in terms of coverage and accuracy. We make our code publicly available to the community.

Problem

• multi-UAV coordination
• 3D reconstruction
• quality-adaptive
• online coordination
• workload redistribution

Innovation

• quality-adaptive
• multi-UAV coordination
• TSDF confidence
• workload redistribution
• decentralized decision-making