SIGN：通过强化学习实现安全的图像目标导航用于自主无人机
SIGN: Safety-Aware Image-Goal Navigation for Autonomous Drones via Reinforcement Learning
AI总结：
本文提出了一种基于强化学习的仿真到现实框架，实现无人机的安全图像目标导航，通过直接速度控制和深度安全模块实现自主探索与避障。
摘要：
图像目标导航（ImageNav）任务要求机器人自主探索未知环境并到达与给定目标图像视觉匹配的地点。尽管先前的工作主要研究地面机器人的ImageNav，但为自主无人机启用此能力更具挑战性，因为无人机需要高频反馈控制和全局定位以保持稳定飞行。在本文中，我们提出了一种新颖的仿真到现实框架，利用强化学习（RL）实现无人机的ImageNav。为了增强视觉表示能力，我们的方法通过辅助任务训练视觉主干，包括图像扰动和未来转换预测，从而产生更有效的策略训练。所提出的算法实现了端到端的ImageNav，通过直接速度控制，消除了对外部定位的需求。此外，我们集成了基于深度的安全部件，用于实时障碍物避障，使无人机能够在拥挤环境中安全导航。与大多数现有无人机导航方法仅关注参考跟踪或障碍物避障不同，我们的框架支持全面的导航行为，包括自主探索、障碍物避障和图像目标寻找，而无需显式全局地图。代码和模型检查点可在https://github.com/Zichen-Yan/SIGN上获得。
英文摘要：
Image-goal navigation (ImageNav) tasks a robot with autonomously exploring an unknown environment and reaching a location that visually matches a given target image. While prior works primarily study ImageNav for ground robots, enabling this capability for autonomous drones is substantially more challenging due to their need for high-frequency feedback control and global localization for stable flight. In this paper, we propose a novel sim-to-real framework that leverages reinforcement learning (RL) to achieve ImageNav for drones. To enhance visual representation ability, our approach trains the vision backbone with auxiliary tasks, including image perturbations and future transition prediction, which results in more effective policy training. The proposed algorithm enables end-to-end ImageNav with direct velocity control, eliminating the need for external localization. Furthermore, we integrate a depth-based safety module for real-time obstacle avoidance, allowing the drone to safely navigate in cluttered environments. Unlike most existing drone navigation methods that focus solely on reference tracking or obstacle avoidance, our framework supports comprehensive navigation behaviors, including autonomous exploration, obstacle avoidance, and image-goal seeking, without requiring explicit global mapping. Code and model checkpoints are available at https://github.com/Zichen-Yan/SIGN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
