本文为美国卡耐基梅隆大学（作者：Sandeep Konam）的硕士论文，共54页。

在本论文中，我们研究基于视觉的技术来支持机器人在人类环境中的移动自主性，包括理解与分类任务相关的重要图像特征。考虑基于视觉的自主性这一广泛目标，该工作将沿着三个主要方面进行。我们的第一种算法使无人机能够对地面移动机器人CoBot进行视觉定位和导航，以执行视觉搜索任务。该方法利用了CoBot强大的定位和导航能力，同时允许无人机在CoBot无法访问的位置搜索感兴趣的对象。第二，为了使用单目相机实现无人机的安全导航，我们设计了一个基于深度学习的感知系统，以实时避免障碍物。我们已经证明，使用我们设计的系统，无人机可以在各种具有挑战性的环境中安全导航。最后，我们的目标是对基于视觉的决策进行解释，提出了一种解释技术来理解基于深度学习的图像分类器预测。我们提供了一种自动补丁模式标记解释（APPLE）算法，用于分析深度网络，找出对网络分类结果“重要”的神经元，并自动标记激活这些重要神经元的输入图像的补丁。我们研究了几种对神经元重要的测量方法，并证明我们的技术可用于深入了解网络如何分解图像以进行分类，最后通过实验结果证明了这些贡献的性能。

In this thesis, we investigate vision-based techniques to support robot mobile autonomy in human environments, including also understanding the important image features with respect to a classification task. Given this wide goal of transparent vision-based autonomy, the work proceeds along three main fronts. Our first algorithm enables a UAV to visually localize and navigate with respect to CoBot, a ground mobile robot, in order to perform visual search tasks. Our approach leverages the robust localization and navigation capabilities of CoBot while allowing the UAV to search for the object of interest in locations that CoBot cannot access. Second, to enable safe UAV navigation using its monocular camera, we contribute a deep learning based perception system to avoid obstacles in real-time. We demonstrate that using our system, UAVs can navigate safely in various challenging environments. Finally, we address our goal towards justification of vision-based decisions. We investigate an explanation technique to understand the predictions of a deep learning based image classifier. We contribute the Automatic Patch Pattern Labeling for Explanation (APPLE) algorithm for analyzing a deep network to find neurons that are ‘important' to the network classification outcome, and for automatically labeling the patches of the input image that activate these important neurons. We investigate several measures of importance for neurons and demonstrate that our technique can be used to gain insight into how a network decomposes an image to make its classification. The performance of each of these contributions is demonstrated through experimental results.

1 引言

2 室内目标搜索任务的无人机与CoBot协同工作

3 基于深度学习的无人机避障

4 自动补丁模式标记的解释（APPLE）算法

5 结论 

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