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Development of dog-like retrieving capability in a ground robot

机译：地面机器人中狗式检索能力的发展

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This paper presents the Mobile Intelligence Team's approach to addressing the CANINE outdoor ground robot competition. The competition required developing a robot that provided retrieving capabilities similar to a dog, while operating fully autonomously in unstructured environments. The vision team consisted of Mobile Intelligence, the Georgia Institute of Technology, and Wayne State University. Important computer vision aspects of the project were the ability to quickly learn the distinguishing characteristics of novel objects, searching images for the object as the robot drove a search pattern, identifying people near the robot for safe operations, correctly identify the object among distractors, and localizing the object for retrieval. The classifier used to identify the objects will be discussed, including an analysis of its performance, and an overview of the entire system architecture presented. A discussion of the robot's performance in the competition will demonstrate the system's successes in real-world testing.
机译：本文介绍了移动智能团队解决CANINE户外地面机器人竞赛的方法。竞赛需要开发一种机器人，该机器人提供类似于狗的检索功能，同时在非结构化环境中完全自主运行。视觉团队由移动智能，乔治亚理工学院和韦恩州立大学组成。该项目在计算机视觉方面的重要方面是能够快速学习新颖对象的区别特征，在机器人驱动搜索模式时搜索对象的图像，识别机器人附近的人以进行安全操作，正确识别干扰对象的能力以及本地化对象以进行检索。将讨论用于识别对象的分类器，包括对其性能的分析以及所呈现的整个系统架构的概述。讨论机器人在比赛中的性能将证明该系统在实际测试中的成功。

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