基于变时滞关键帧里程法的高动态飞行系统状态估计

Korbinian Schmid, Felix Ruess, M. Suppa, Darius Burschka
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引用次数: 45

摘要

系统状态估计是机器人导航和控制的重要组成部分。惯性导航系统(INS)和其他外感传感器(如摄像头或激光扫描仪)的组合被广泛使用。在载荷、功耗和计算资源有限的小型机器人系统中,外感传感器数据的处理通常会引入时间延迟,这在传感器数据融合过程中必须加以考虑。这些时间延迟在估计系统速度时尤为重要。在本文中,我们提出了一种融合了时滞、相对外感知传感器测量的惯性系统状态估计框架。我们评估了它的性能高动态飞行系统的轨迹,包括翻转。蒙特卡罗模拟显示了在15Hz到1Hz的测量频率范围内,速度误差和位置误差随时间延迟的变化规律。基于关键帧里程计的滤波算法允许最优的、局部无漂移的导航,同时仍然在小型机载计算机上计算易于处理。最后,我们提出的结果,该算法应用于一个真正的四旋翼从房子里飞出来通过窗口。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State estimation for highly dynamic flying systems using key frame odometry with varying time delays
System state estimation is an essential part for robot navigation and control. A combination of Inertial Navigation Systems (INS) and further exteroceptive sensors such as cameras or laser scanners is widely used. On small robotic systems with limitations in payload, power consumption and computational resources the processing of exteroceptive sensor data often introduces time delays which have to be considered in the sensor data fusion process. These time delays are especially critical in the estimation of system velocity. In this paper we present a state estimation framework fusing an INS with time delayed, relative exteroceptive sensor measurements. We evaluate its performance for a highly dynamic flight system trajectory including a flip. The evolution of velocity and position errors for varying measurement frequencies from 15Hz to 1Hz and time delays up to 1s is shown in Monte Carlo simulations. The filter algorithm with key frame based odometry permits an optimal, local drift free navigation while still being computationally tractable on small onboard computers. Finally, we present the results of the algorithm applied to a real quadrotor by flying from inside a house out through the window.
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