Agile quadrotor flight pushes the limits of control, actuation, and onboard perception. While time-optimal trajectory planning has been extensively studied, existing approaches typically neglect the tight coupling between vehicle dynamics, environmental geometry, and the visual requirements of onboard state estimation. As a result, trajectories that are dynamically feasible may fail in closed-loop execution due to degraded visual quality. This paper introduces a unified time-optimal trajectory optimization framework for vision-based quadrotors that explicitly incorporates perception constraints alongside full nonlinear dynamics, rotor actuation limits, aerodynamic effects, camera field-of-view constraints, and convex geometric gate representations. The proposed formulation solves minimum-time lap trajectories for arbitrary racetracks with diverse gate shapes and orientations, while remaining numerically robust and computationally efficient. We derive an information-theoretic position uncertainty metric to quantify visual state-estimation quality and integrate it into the planner through three perception objectives: position uncertainty minimization, sequential field-of-view constraints, and look-ahead alignment. This enables systematic exploration of the trade-offs between speed and perceptual reliability. To accurately track the resulting perception-aware trajectories, we develop a model predictive contouring tracking controller that separates lateral and progress errors. Experiments demonstrate real-world flight speeds up to 9.8 m/s with 0.07 m average tracking error, and closed-loop success rates improved from 55% to 100% on a challenging Split-S course. The proposed system provides a scalable benchmark for studying the fundamental limits of perception-aware, time-optimal autonomous flight.

翻译：敏捷四旋翼飞行在控制、驱动和机载感知方面均面临极限挑战。尽管时间最优轨迹规划已得到广泛研究，但现有方法通常忽略了飞行器动力学、环境几何结构与机载状态估计视觉需求之间的紧密耦合。因此，动态可行的轨迹可能因视觉质量下降而在闭环执行中失败。本文提出了一种基于视觉的四旋翼统一时间最优轨迹优化框架，该框架将感知约束与完全非线性动力学、旋翼驱动限制、空气动力学效应、相机视场约束以及凸几何门框表征进行显式整合。所提出的公式能够为具有不同形状和朝向门框的任意赛道求解最小时间圈数轨迹，同时保持数值鲁棒性和计算效率。我们推导出基于信息论的位置不确定性度量来量化视觉状态估计质量，并通过三个感知目标将其整合至规划器中：位置不确定性最小化、序列视场约束和前视对齐。这使得系统能够探索速度与感知可靠性之间的权衡关系。为精确跟踪生成的感知感知轨迹，我们开发了一种模型预测轮廓跟踪控制器，该控制器可分离横向误差与进程误差。实验证明，在真实飞行中速度可达9.8 m/s，平均跟踪误差为0.07 m，在具有挑战性的Split-S赛道上闭环成功率从55%提升至100%。所提出的系统为研究感知感知时间最优自主飞行的根本极限提供了可扩展的基准。