基于视觉的MPPI用于敏捷无人机竞速：通过神经签名距离场导航任意门姿态
Vision-Guided MPPI for Agile Drone Racing: Navigating Arbitrary Gate Poses via Neural Signed Distance Fields
AI总结：
本文提出基于视觉的MPPI框架，利用神经签名距离场实现无需参考的敏捷无人机飞行，有效应对动态变化的赛道和门姿态。
摘要：
自主无人机竞速需要在极端敏捷性下紧密耦合感知、规划和控制。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，使其对空间扰动、未建模的赛道变化和传感器噪声敏感。相反，端到端学习策略经常过拟合特定赛道布局，并在零样本泛化方面遇到困难。为了解决这些根本限制，我们提出了一种完全在机的、基于视觉的最优控制框架，该框架通过任意放置和定向的门实现参考自由的敏捷飞行。我们方法的核心是Gate-SDF，一种新颖的隐式学习神经签名距离场。Gate-SDF直接处理原始、嘈杂的深度图像，以预测连续的空间场，该场既提供碰撞排斥，又提供主动的几何引导，指向有效的通行区域。我们将这种表示无缝集成到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，该框架能够在实时情况下同时评估这些连续空间约束，通过数千次模拟轨迹滚动。此外，我们的公式本质上保持了空间一致性，确保在激烈操作期间即使在严重的视觉遮挡下也能实现稳健导航。广泛的模拟和现实世界实验表明，所提出系统实现了高速敏捷飞行，并成功导航未见过的赛道，即使存在严重的未建模门位移和方向扰动。视频可在https://zhaofangguo.github.io/vision_guided_mppi/上获得。
英文摘要：
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/
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。
