发表机构
Inria, INSA Lyon, CITI（法国国家信息与自动化研究所、里昂国立应用科学学院、CITI）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无人机三维重建中计算和能量受限及协调难问题，提出质量自适应分散决策策略，集成质量导向准则，采用两级协调，仿真显示该方法提高路径效率且实现更高保真度重建，代码公开。
AI 中文摘要
未知环境的三维重建是机器人技术中的关键应用，但受当前航空平台计算和能量能力严重限制。部署多架无人机并提供有效路径规划策略是常用方法，但无人机间有效在线协调仍是重大挑战。为解决此问题，我们提出质量自适应分散决策策略以构建用户定义保真度的三维地图。该方法将基于TSDF置信度的质量导向准则集成到视图生成和信息增益估计中，以生成符合所需保真度目标的视点。此外，采用两级协调：视点评估中的惩罚因子鼓励无人机局部分散，全局不平衡校正机制基于正则化聚类和最优任务分配，仅在检测到相对于高信息区域的不平衡配置时触发。仿真结果表明，与现有多无人机探索方法相比，该方法提高了路径效率，在覆盖范围和精度方面实现了更高保真度的重建。我们将代码公开供社区使用。
英文摘要
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.
