Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles
Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles
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DOI：
10.1016/j.robot.2009.02.001
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发表时间：
2009-06-30
影响因子：
4.3
通讯作者：
Nonami, Kenzo
中科院分区：
文献类型：
--
作者：
Kendoul, Farid;Fantoni, Isabelle;Nonami, Kenzo
本文所考虑的问题是基于视觉的小微无人机自动驾驶仪的设计。所提出的自动驾驶仪基于基于光流的视觉系统用于自主定位和场景映射，以及用于飞行控制和制导的非线性控制系统。利用低分辨率的机载相机和低成本的惯性测量单元(IMU)，开发了一种实时的三维视觉算法，用于估计光流、飞机自身运动和深度图。我们的实现基于3个嵌套卡尔曼滤波器(3NKF)，并导致了一个高效和稳健的估计过程。视觉和控制算法已在一架四旋翼无人机上实现，并在实时飞行试验中进行了演示。实验结果表明，所提出的基于视觉的自动驾驶仪能够利用从光流中提取的信息来实现小型旋翼机的全自主飞行。(C)2009爱思唯尔B.V.保留所有权利。
The problem considered in this paper involves the design of a vision-based autopilot for small and micro Unmanned Aerial Vehicles (UAVs). The proposed autopilot is based on an optic flow-based vision system for autonomous localization and scene mapping, and a nonlinear control system for flight control and guidance. This paper focusses on the development of a real-time 3D vision algorithm for estimating optic flow, aircraft self-motion and depth map, using a low-resolution onboard camera and a low-cost Inertial Measurement Unit (IMU). Our implementation is based on 3 Nested Kalman Filters (3NKF) and results in an efficient and robust estimation process. The vision and control algorithms have been implemented on a quadrotor UAV, and demonstrated in real-time flight tests. Experimental results show that the proposed vision-based autopilot enabled a small rotorcraft to achieve fully-autonomous flight using information extracted from optic flow. (C) 2009 Elsevier B.V. All rights reserved.