发表机构
Czech Technical University in Prague（布拉格捷克理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多无人机敏捷飞行，提出利用视觉检测器提供的倾斜测量进行姿态感知状态估计，通过线性推力约束卡尔曼滤波器，将速度和加速度误差分别降低40%和57%，并实现超过2g加速度的跟踪。
AI 中文摘要
敏捷的多无人机飞行需要准确且低延迟的机载估计，以获取邻近无人机的运动学状态，用于碰撞避免、运动协调等。大多数基于视觉的方法依赖于仅位置测量，通过位移间接推断速度和加速度。我们表明，这在高阶状态估计中引入了固定的结构延迟，限制了可实现的敏捷性。为解决这一问题，我们提出集成倾斜测量，该测量由最先进的视觉检测器提供，能够告知共面多旋翼无人机的推力方向。我们在两个真实世界数据集和一个高保真照片级模拟数据集上，针对不同敏捷水平（3-21 m/s^2），对四种仅位置估计器和五种姿态感知估计器（包括一种新颖的线性推力约束卡尔曼滤波器公式）进行了基准测试。在我们的设置中，姿态感知估计在三个数据集上一致地将平均速度和加速度估计误差分别降低了40%和57%，其中所提出的卡尔曼滤波器公式优于其他估计器。仅位置滤波器在加速度阶跃响应中表现出恒定的约300毫秒延迟，且与敏捷性无关，而倾斜约束估计器通过观察推力方向的变化（在位移积累之前）接近由相机帧率给出的物理响应极限。在采用NMPC控制的闭环领导者-跟随者模拟实验中，对领导者状态的仅位置估计无法使跟随者稳定悬停，而所提出的估计器能够跟踪超过2g加速度的横向机动。
英文摘要
Agile multi-UAV flight requires accurate and low-latency onboard estimation of the kinematic states of neighboring UAVs for collision avoidance, motion coordination, etc. Most vision-based approaches rely on position-only measurements, inferring velocity and acceleration indirectly from displacement. We show that this introduces a fixed structural delay in the estimation of higher-order states, which limits the achievable agility. To address this, we propose to integrate tilt measurements, provided by a state-of-the-art visual detector, which inform about the thrust direction of co-planar multirotor UAVs. We benchmark four position-only and five pose-aware estimators, including a novel formulation of a linear thrust-constraining Kalman filter, on two real-world and one high-fidelity photorealistic simulated dataset over different levels of agility (3-21 m/s^2). In our setup, pose-aware estimation consistently reduces the average velocity and acceleration estimation errors by 40% and 57% across the three datasets with the proposed KF formulation outperforming the other estimators. Position-only filters exhibit a constant ~300 ms delay in acceleration step response independent of agility, whereas the tilt-constrained estimators operate near the physical response limit given by the camera frame-rate by observing the change in thrust direction before the displacement accumulates. In a closed-loop leader-follower simulated experiment with NMPC control, position-only estimation of the leader's state fails to facilitate stable hovering of the follower, while the proposed estimator enables tracking of lateral maneuvers exceeding 2g of acceleration.
CommentsThis work has been accepted to the IEEE for possible publication (IROS 2026)
