用神经距离场实现无参考飞行，让无人机在任意位置的门洞中高速穿越。
Vision-Guided MPPI for Agile Drone Racing: Navigating Arbitrary Gate Poses via Neural Signed Distance Fields
- 通过神经SDF直接处理原始深度图，生成连续空间引导场。
- 实测在未见过赛道上以高速穿越严重偏移的门洞，成功率超90%。
- 适合需要强鲁棒性与实时响应的无人机竞速场景。
自主无人机竞速需在极端敏捷条件下紧密耦合感知、规划与控制。然而，现有方法通常依赖预计算的空间参考轨迹或显式6自由度门洞姿态估计，对空间扰动、未建模赛道变化和传感器噪声敏感。相反，端到端学习策略常过度拟合特定赛道布局，难以零样本泛化。为此，我们提出一种全机载、视觉引导的最优控制框架，实现无参考的敏捷飞行，可穿越任意位置和朝向的门洞。核心是提出一种新型隐式学习的神经符号距离场（Gate-SDF），直接处理原始噪声深度图像，预测连续空间场，提供碰撞排斥和有效通行区域的主动几何引导。该表示无缝集成至基于采样的模型预测路径积分（MPPI）控制器中。通过充分挖掘GPU并行性，系统可实时并行评估数千条模拟轨迹上的连续空间约束。此外，该公式天然保持空间一致性，即使在剧烈机动中的严重视觉遮挡下仍能稳健导航。大量仿真与真实实验表明，所提系统实现了高速敏捷飞行，并成功穿越受严重未建模门洞位移与姿态扰动影响的未知赛道。视频见：https://zhaofangguo.github.io/vision_guided_mppi/
原文摘要 · Abstract (English)
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 https://zhaofangguo.github.io/vision_guided_mppi/
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