自主无人机在无标记场地实现职业级竞速，性能媲美人类高手。
On Your Own: Pro-level Autonomous Drone Racing in Uninstrumented Arenas
- 基于视觉的自主系统，在有无外部追踪条件下均能运行
- 在未标定环境中实现与专业飞行员相当的竞速表现
- 适合真实场景部署，突破实验室环境限制
无人机技术正广泛应用于农业、物流、国防、基础设施和环境监测等领域。视觉自主是其实现现实应用的关键，尤其在传统导航失效的陌生非结构化环境中。自主无人机竞速已成为此类系统性能的行业基准。当前最先进方法已证明，自主系统可在竞赛场中超越人类水平。但其在商业与野外作业中的实际应用仍受限，因多数系统在高度控制环境下训练与评估。本文分析了系统在受控环境（具备外部跟踪用于真值对比）下的能力，并在极具挑战性的无仪器环境（无真值测量）中进行了验证。结果表明，本方法在两种场景下均可达到专业人类飞行员的竞速水平。
原文摘要 · Abstract (English)
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.
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