Drone technology is proliferating in many industries, including agriculture, logistics, defense, infrastructure, and environmental monitoring. Vision-based autonomy is one of its key enablers, particularly for real-world applications. This is essential for operating in novel, unstructured environments where traditional navigation methods may be unavailable. Autonomous drone racing has become the de facto benchmark for such systems. State-of-the-art research has shown that autonomous systems can surpass human-level performance in racing arenas. However, the direct applicability to commercial and field operations is still limited, as current systems are often trained and evaluated in highly controlled environments. In our contribution, the system's capabilities are analyzed within a controlled environment -- where external tracking is available for ground-truth comparison -- but also demonstrated in a challenging, uninstrumented environment -- where ground-truth measurements were never available. We show that our approach can match the performance of professional human pilots in both scenarios.

翻译：无人机技术正广泛应用于农业、物流、国防、基础设施及环境监测等诸多领域。基于视觉的自主飞行技术是其关键赋能因素之一，尤其对于现实世界的应用场景至关重要。在传统导航方法可能无法使用的新颖、非结构化环境中，这项技术显得尤为必要。自主无人机竞速已成为此类系统事实上的基准测试。前沿研究表明，自主系统在竞速赛道中已能超越人类水平的表现。然而，由于现有系统通常在高度受控环境中进行训练与评估，其直接应用于商业及实地作业的能力仍受限。在本研究中，我们不仅分析了系统在受控环境中的能力——该环境下可通过外部追踪获取真实轨迹数据进行对比——更在极具挑战性的非仪器化环境中进行了验证，该环境从未提供真实轨迹测量数据。我们证明，所提出的方法在两种场景下均能达到专业人类飞手的操作水平。