Abstract
Autonomous drone racing requires the tight coupling of perception, planning, and control under extreme agility. However, recent approaches typically rely on precomputed spatial reference trajectories or explicit 6-DoF gate pose estimation, rendering them brittle to spatial perturbations, unmodeled track changes, and sensor noise. Conversely, end-to-end learning policies frequently overfit to specific track layouts and struggle with zero-shot generalization. To address these fundamental limitations, we propose a fully onboard, vision guided optimal control framework that enables reference-free agile flight through arbitrarily placed and oriented gates. Central to our approach is Gate-SDF, a novel, implicitly learned neural signed distance field. Gate-SDF directly processes raw, noisy depth images to predict a continuous spatial field that provides both collision repulsion and active geometric guidance toward the valid traversal area. We seamlessly integrate this representation into a sampling-based Model Predictive Path Integral (MPPI) controller. By fully exploiting GPU parallelism, the framework evaluates these continuous spatial constraints across thousands of simulated trajectory rollouts simultaneously in real time. Furthermore, our formulation inherently maintains spatial consistency, ensuring robust navigation even under severe visual occlusion during aggressive maneuvers. Extensive simulations and real-world experiments demonstrate that the proposed system achieves high-speed agile flight and successfully navigates unseen tracks subject to severe unmodeled gate displacements and orientation perturbations. Videos are available at this https URL
Abstract (translated)
自主无人机竞速要求在极高灵活性条件下，感知、规划和控制的紧密耦合。然而，最近的方法通常依赖于预计算的空间参考轨迹或显式的6自由度门姿态估计，这使它们对空间扰动、未建模赛道变化及传感器噪声非常敏感。相比之下，端到端学习策略往往会过度拟合并难以实现零样本泛化能力。为了解决这些根本性的限制，我们提出了一种完全在机载设备上运行的、由视觉引导的最优控制框架，该框架能够通过任意放置和定向的门进行无参考自由飞行。 我们的方法的核心是Gate-SDF，这是一种新颖的隐式学习神经签名距离场。Gate-SDF可以直接处理原始且含噪的深度图像，并预测一个连续的空间场，提供碰撞排斥以及对有效穿越区域的主动几何引导。我们将这种表示无缝地整合到了基于采样的模型预测路径积分（MPPI）控制器中。通过充分利用GPU并行性，该框架能够在实时状态下同时评估数千个模拟轨迹回放下的连续空间约束条件。此外，我们的方法固有地保持了空间一致性，在进行剧烈机动操作时即使遭遇严重视觉遮挡也能确保稳健导航。 广泛的仿真和现实世界实验表明，所提出的系统能够实现高速灵巧飞行，并成功地在存在严重未建模门位移及姿态扰动的未知赛道上导航。视频可在提供的链接中找到（请将“this https URL”替换为实际的视频网址）。
URL
https://arxiv.org/abs/2603.07199
