自主驾驶：在无仪器赛场上的专业级无人机竞速
On Your Own: Pro-level Autonomous Drone Racing in Uninstrumented Arenas
AI总结：
该研究提出了一种在无仪器赛场中实现专业级无人机竞速的自主系统，通过受控环境与挑战性环境的对比验证了其性能。
摘要：
无人机技术在许多行业中迅速普及，包括农业、物流、国防、基础设施和环境监测。基于视觉的自主性是其关键使能器，尤其对于现实世界应用而言。这在操作于新型、无结构环境中至关重要，其中传统导航方法可能不可用。自主无人机竞速已成为此类系统的事实基准。最新研究显示，自主系统在竞速赛场上的表现可超越人类水平。然而，直接应用于商业和现场操作仍有限，因为当前系统通常在高度受控环境中训练和评估。在我们的贡献中，系统的能力在受控环境中分析——其中外部跟踪可用于地面真实比较——同时也在具有挑战性的无仪器环境中展示——其中地面真实测量从未可用。我们展示了我们的方法在两种情况下都能与专业人类飞行员的性能相匹配。
英文摘要：
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.
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。
