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Vision-based navigation and mapping for flight in gps-denied environments .

机译：基于GPS的环境中飞行的基于视觉的导航和制图。

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摘要

Traditionally, the task of determining aircraft position and attitude for automatic control has been handled by the combination of an inertial measurement unit (IMU) with a Global Positioning System (GPS) receiver. In this configuration, accelerations and angular rates from the IMU can be integrated forward in time, and position updates from the GPS can be used to bound the errors that result from this integration. However, reliance on the reception of GPS signals places artificial constraints on aircraft such as small unmanned aerial vehicles (UAVs) that are otherwise physically capable of operation in indoor, cluttered, or adversarial environments.;Therefore, this work investigates methods for incorporating a monocular vision sensor into a standard avionics suite. Vision sensors possess the potential to extract information about the surrounding environment and determine the locations of features or points of interest. Having mapped out landmarks in an unknown environment, subsequent observations by the vision sensor can in turn be used to resolve aircraft position and orientation while continuing to map out new features.;An extended Kalman filter framework for performing the tasks of vision-based mapping and navigation is presented. Feature points are detected in each image using a Harris corner detector, and these feature measurements are corresponded from frame to frame using a statistical Z-test. When GPS is available, sequential observations of a single landmark point allow the point's location in inertial space to be estimated. When GPS is not available, landmarks that have been sufficiently triangulated can be used for estimating vehicle position and attitude.;Simulation and real-time flight test results for vision-based mapping and navigation are presented to demonstrate feasibility in real-time applications. These methods are then integrated into a practical framework for flight in GPS-denied environments and verified through the autonomous flight of a UAV during a loss-of-GPS scenario. The methodology is also extended to the application of vehicles equipped with stereo vision systems. This framework enables aircraft capable of hovering in place to maintain a bounded pose estimate indefinitely without drift during a GPS outage.
机译：传统上，确定飞机位置和姿态以进行自动控制的任务是通过惯性测量单元（IMU）与全球定位系统（GPS）接收器的组合来完成的。在这种配置下，IMU的加速度和角速度可以及时向前集成，而GPS的位置更新可以用来限制这种集成所导致的误差。但是，依赖于GPS信号的接收对诸如小型无人驾驶飞机（UAV）之类的飞机施加了人工约束，这些飞机原本可以在室内，杂乱或敌对环境中运行;因此，这项工作研究了结合单眼的方法视觉传感器成为标准的航空电子套件。视觉传感器具有提取周围环境信息并确定特征或兴趣点位置的潜力。在未知环境中绘制了地标后，视觉传感器的后续观察结果可用于解析飞机的位置和方向，同时继续绘制新特征。扩展的卡尔曼滤波器框架，用于执行基于视觉的地图绘制和导航显示。使用哈里斯拐角检测器在每个图像中检测特征点，并使用统计Z检验在帧与帧之间对应这些特征测量值。当GPS可用时，对单个地标点的连续观测可以估计该点在惯性空间中的位置。当无法使用GPS时，已充分三角化的地标可用于估计车辆的位置和姿态。提出了基于视觉的制图和导航的模拟和实时飞行测试结果，以证明在实时应用中的可行性。然后将这些方法集成到在GPS拒绝环境中飞行的实用框架中，并通过在GPS丢失情况下无人机的自主飞行进行验证。该方法还扩展到配备立体视觉系统的车辆的应用。该框架使能够悬停在适当位置的飞机能够在GPS中断期间无限期地保持边界姿态估计而不会发生漂移。

著录项

  • 作者

    Wu, Allen D.;

  • 作者单位

    Georgia Institute of Technology.;

  • 授予单位 Georgia Institute of Technology.;
  • 学科 Engineering Aerospace.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 104 p.
  • 总页数 104
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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