本文为美国杨百翰大学（作者：Barrett Bruce Edwards）的硕士论文，共109页。

小型无人机（UAV）作为一个稳定的平台用于特定的领域，其可行性在近年来已经得到了显著提高。轻型无人机开发的重点最初是创造一种能够稳定可控飞行的飞行器。这在很大程度上是一个已解决的问题。目前的无人驾驶飞机甚至可以放在背包里，采用手动发射，重量只有几磅，能够在无限制的空域中飞行。

无人机最基本的用途是观察周围环境，并利用这些信息来影响决策。先前尝试使用视觉信息控制小型无人机时，采用了非机载实时处理的方法，将机载摄像头的视频流传输到地面站进行处理和决策。由于信息传输时间在时间敏感控制算法中引入了不可接受的延迟量，这些尝试取得的成果有限。机载实时图像处理提供了一个低延迟的解决方案，可以避免无人机与地面站之间双向通信延迟带来的负面影响。

本文的第一部分将说明机载视觉处理能够满足自主无人机的实时控制要求，包括对潜在机载计算平台的评估，基于FPGA的图像处理技术将成为轻型无人飞机的理想方案。本论文的第二部分将重点研究两种概念验证应用的精确车载视觉系统实现。第一个应用描述了使用机器视觉算法来定位、跟踪无人机的目标着陆点。目前的GPS导航不足以完成这项任务。在下降着陆过程中利用视觉系统定位目标位置，并向无人机提供航向修正更新。第二个应用描述了一个特征检测和跟踪子系统，该子系统可用于高级应用算法中。

The viability of small Unmanned Aerial Vehicles (UAVs) as a stable platform for specific application use has been significantly advanced in recent years. Initial focus of lightweight UAV development was to create a craft capable of stable and controllable flight. This is largely a solved problem. Currently, the field has progressed to the point that unmanned aircraft can be carried in a backpack, launched by hand, weigh only a few pounds and be capable of navigating through unrestricted airspace. The most basic use of a UAV is to visually observe the environment and use that information to influence decision making. Previous attempts at using visual information to control a small UAV used an off-board approach where the video stream from an onboard camera was transmitted down to a ground station for processing and decision making. These attempts achieved limited results as the two-way transmission time introduced unacceptable amounts of latency into time-sensitive control algorithms. Onboard image processing offers a low-latency solution that will avoid the negative effects of two-way communication to a ground station. The first part of this thesis will show that onboard visual processing is capable of meeting the real-time control demands of an autonomous vehicle, which will also include the evaluation of potential onboard computing platforms. FPGA-based image processing will be shown to be the ideal technology for lightweight unmanned aircraft. The second part of this thesis will focus on the exact onboard vision system implementation for two proof-of-concept applications. The first application describes the use of machine vision algorithms to locate and track a target landing site for a UAV. GPS guidance was insufficient for this task. A vision system was utilized to localize the target site during approach and provide course correction updates to the UAV. The second application describes a feature detection and tracking sub-system that can be used in higher level application algorithms.

1 引言

2 项目背景

3 机载平台选择

4 系统结构

5 应用：基于视觉的机载着陆系统

6 应用：特征跟踪

7 结论 

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